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The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It combines the Norwegian Earth System Model version 1 (NorESM1) – which features interactive aerosol–cloud schemes and an isopycnic-coordinate ocean component with biogeochemistry – with anomaly assimilation of sea surface temperature (SST) and T/S-profile observations using the ensemble Kalman filter (EnKF). We describe the Earth system component and the data assimilation (DA) scheme, highlighting implementation of new forcings, bug fixes, retuning and DA innovations. Notably, NorCPM1 uses two anomaly assimilation variants to assess the impact of sea ice initialization and climatological reference period: the first (i1) uses a 1980–2010 reference climatology for computing anomalies and the DA only updates the physical ocean state; the second (i2) uses a 1950–2010 reference climatology and additionally updates the sea ice state via strongly coupled DA of ocean observations. We assess the baseline, reanalysis and prediction performance with output contributed to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). The NorESM1 simulations exhibit a moderate historical global surface temperature evolution and tropical climate variability characteristics that compare favourably with observations. The climate biases of NorESM1 using CMIP6 external forcings are comparable to, or slightly larger than those of, the original NorESM1 CMIP5 model, with positive biases in Atlantic meridional overturning circulation (AMOC) strength and Arctic sea ice thickness, too-cold subtropical oceans and northern continents, and a too-warm North Atlantic and Southern Ocean. The biases in the assimilation experiments are mostly unchanged, except for a reduced sea ice thickness bias in i2 caused by the assimilation update of sea ice, generally confirming that the anomaly assimilation synchronizes variability without changing the climatology. The i1 and i2 reanalysis/hindcast products overall show comparable performance. The benefits of DA-assisted initialization are seen globally in the first year of the prediction over a range of variables, also in the atmosphere and over land. External forcings are the primary source of multiyear skills, while added benefit from initialization is demonstrated for the subpolar North Atlantic (SPNA) and its extension to the Arctic, and also for temperature over land if the forced signal is removed. Both products show limited success in constraining and predicting unforced surface ocean biogeochemistry variability. However, observational uncertainties and short temporal coverage make biogeochemistry evaluation uncertain, and potential predictability is found to be high. For physical climate prediction, i2 performs marginally better than i1 for a range of variables, especially in the SPNA and in the vicinity of sea ice, with notably improved sea level variability of the Southern Ocean. Despite similar skills, i1 and i2 feature very different drift behaviours, mainly due to their use of different climatologies in DA; i2 exhibits an anomalously strong AMOC that leads to forecast drift with unrealistic warming in the SPNA, whereas i1 exhibits a weaker AMOC that leads to unrealistic cooling. In polar regions, the reduction in climatological ice thickness in i2 causes additional forecast drift as the ice grows back. Posteriori lead-dependent drift correction removes most hindcast differences; applications should therefore benefit from combining the two products. The results confirm that the large-scale ocean circulation exerts strong control on North Atlantic temperature variability, implying predictive potential from better synchronization of circulation variability. Future development will therefore focus on improving the representation of mean state and variability of AMOC and its initialization, in addition to upgrades of the atmospheric component. Other efforts will be directed to refining the anomaly assimilation scheme – to better separate internal and forced signals, to include land and atmosphere initialization and new observational types – and improving biogeochemistry prediction capability. Combined with other systems, NorCPM1 may already contribute to skilful multiyear climate prediction that benefits society.

After an unprecedented retreat, the total Arctic sea ice cover for the post-2007 period is characterized by low extent and a remarkable increase in annual cycle amplitude. We have identified the leading role of spring Bering Sea ice in explaining the changes in the amplitude of the annual cycle of total Arctic sea ice. In particular, these changes are related to the recent occurrence of multiyear variability in spring Bering Sea ice extent. This is due to the phase-locking of the North Pacific Gyre Oscillation (NPGO) and the Pacific Decadal Oscillation (PDO) after about 2007, with a correlation coefficient reaching −0.6. Furthermore, there emerge notable changes in the sea level pressure and sea surface temperature patterns associated with the NPGO in the recent decade. After 2007, the NPGO is related to a quadrupole of sea level pressure (SLP) anomalies that is associated with the wind stress curl and Ekman pumping rate anomalies in the Bering deep basin; these account for the change in Bering Sea subsurface variability that contribute to the decadal oscillation of the spring Bering Sea ice extent.

Abstract There is a growing demand for skillful prediction systems in the Arctic. Using the Norwegian Climate Prediction Model that combines the fully coupled Norwegian Earth System Model and the Ensemble Kalman filter, we present a system that performs both, weakly coupled data assimilation (wCDA) when assimilating ocean hydrography (by updating the ocean alone) and strongly coupled data assimilation when assimilating sea ice concentration (SIC) (by jointly updating the sea ice and ocean). We assess the seasonal prediction skill of this version of the Norwegian Climate Prediction Model, the first climate prediction system using strongly coupled data assimilation, by performing retrospective predictions (hindcasts) for the period 1985 to 2010. To better understand the origins of the prediction skill of Arctic sea ice, we compare this version with a version that solely performs wCDA of ocean hydrography. The reanalysis that assimilates just ocean data exhibits skillful hydrography in the upper Arctic Ocean and features an improved sea ice state, such as improved summer SIC in the Barents Sea, or reduced biases in sea ice thickness. Skillful prediction of SIE up to 10–12 lead months are only found during winter in regions of a relatively deep ocean mixed layer outside the Arctic basin. Additional DA of SIC data notably further corrects the initial seaice state, confirming the applicability of the results of Kimmritz et al. (2018) in a historical setting. The resulting prediction skill of SIE is widely enhanced compared to predictions initialized through wCDA of only ocean data. Particularly high skill is found for July‐initialized autumn SIE predictions.

Recent studies demonstrate that weather and climate predictions potentially improve by dynamically combining different models into a so-called “supermodel”. Here, we focus on the weighted supermodel – the supermodel's time derivative is a weighted superposition of the time derivatives of the imperfect models, referred to as weighted supermodeling. A crucial step is to train the weights of the supermodel on the basis of historical observations. Here, we apply two different training methods to a supermodel of up to four different versions of the global atmosphere–ocean–land model SPEEDO. The standard version is regarded as truth. The first training method is based on an idea called cross pollination in time (CPT), where models exchange states during the training. The second method is a synchronization-based learning rule, originally developed for parameter estimation. We demonstrate that both training methods yield climate simulations and weather predictions of superior quality as compared to the individual model versions. Supermodel predictions also outperform predictions based on the commonly used multi-model ensemble (MME) mean. Furthermore, we find evidence that negative weights can improve predictions in cases where model errors do not cancel (for instance, all models are warm with respect to the truth). In principle, the proposed training schemes are applicable to state-of-the-art models and historical observations. A prime advantage of the proposed training schemes is that in the present context relatively short training periods suffice to find good solutions. Additional work needs to be done to assess the limitations due to incomplete and noisy data, to combine models that are structurally different (different resolution and state representation, for instance) and to evaluate cases for which the truth falls outside of the model class.

Wind energy has seen large deployment and substantial cost reductions over the last decades. Further ambitious upscaling is urgently needed to keep the goals of the Paris Agreement within reach. While the variability in wind power generation poses a challenge to grid integration, much progress in quantifying, understanding and managing it has been made over the last years. Despite this progress, relevant modes of variability in energy generation have been overlooked. Based on long-term reanalyses of the 20th century, we demonstrate that multidecadal wind variability has significant impact on wind energy generation in Germany. These modes of variability can not be detected in modern reanalyses that are typically used for energy applications because modern reanalyses are too short (around 40 years of data). We show that energy generation over a 20-year wind park lifetime varies by around ±5 % and the summer-to-winter ratio varies by around ±15 %. Moreover, ERA-Interim-based annual and winter generations are biased high as the period 1979–2010 overlaps with a multidecadal maximum of wind energy generation. The induced variations in wind park lifetime revenues are on the order of 10 % with direct implications for profitability. Our results suggest rethinking energy system design as an ongoing and dynamic process. Revenues and seasonalities change on a multidecadal timescale, and so does the optimum energy system layout.

The tropical Atlantic is home to multiple coupled climate variations covering a wide range of timescales and impacting societally relevant phenomena such as continental rainfall, Atlantic hurricane activity, oceanic biological productivity, and atmospheric circulation in the equatorial Pacific. The tropical Atlantic also connects the southern and northern branches of the Atlantic meridional overturning circulation and receives freshwater input from some of the world’s largest rivers. To address these diverse, unique, and interconnected research challenges, a rich network of ocean observations has developed, building on the backbone of the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA). This network has evolved naturally over time and out of necessity in order to address the most important outstanding scientific questions and to improve predictions of tropical Atlantic severe weather and global climate variability and change. The tropical Atlantic observing system is motivated by goals to understand and better predict phenomena such as tropical Atlantic interannual to decadal variability and climate change; multidecadal variability and its links to the meridional overturning circulation; air-sea fluxes of CO2 and their implications for the fate of anthropogenic CO2; the Amazon River plume and its interactions with biogeochemistry, vertical mixing, and hurricanes; the highly productive eastern boundary and equatorial upwelling systems; and oceanic oxygen minimum zones, their impacts on biogeochemical cycles and marine ecosystems, and their feedbacks to climate. Past success of the tropical Atlantic observing system is the result of an international commitment to sustained observations and scientific cooperation, a willingness to evolve with changing research and monitoring needs, and a desire to share data openly with the scientific community and operational centers. The observing system must continue to evolve in order to meet an expanding set of research priorities and operational challenges. This paper discusses the tropical Atlantic observing system, including emerging scientific questions that demand sustained ocean observations, the potential for further integration of the observing system, and the requirements for sustaining and enhancing the tropical Atlantic observing system.

Benguela Niños are extreme warm events that typically occur during the main downwelling season (austral fall) in the tropical Angolan upwelling system when the biological productivity is low. However, the extreme warm event that occurred between April and August 2021 stands out due to its late timing. It occurred and peaked during the main upwelling season in austral winter with sea surface temperature anomalies exceeding 2 °C in the Angola-Benguela area in June 2021. This led to an unprecedented reduction of the net primary production off southern Angola. Both local atmospheric processes and remote influences (via downwelling coastal trapped wave propagations) have contributed to the onset of the extreme warm event in April and its intensification towards the peak phase in June. Moreover, the poleward advection of warm equatorial waters toward the Angola-Benguela area in May 2021 might have contributed to the warming, since the transport of the Angola Current, as estimated from observations, was notably elevated, amounting to 2.1 Sv., (© 2024. The Author(s).)

Climate change is recognised to lead to spatial shifts in the distribution of small pelagic fish, likely by altering their environmental optima. Fish supply along the Northwest African coast is significant at both socio-economic and cultural levels. Evaluating the impacts of climatic change on small pelagic fish is a challenge and of serious concern in the context of shared stock management. Evaluating the impact of climate change on the distribution of small pelagic fish, a trend analysis was conducted using data from 2363 trawl samplings and 170,000 km of acoustics sea surveys. Strong warming is reported across the Southern Canary Current Large Marine Ecosystem (CCLME), extending from Morocco to Senegal. Over 34 years, several trends emerged, with the southern CCLME experiencing increases in both wind speed and upwelling intensity, particularly where the coastal upwelling was already the strongest. Despite upwelling-induced cooling mechanisms, sea surface temperature (SST) increased in most areas, indicating the complex interplay of climatic-related stressors in shaping the marine ecosystem. Concomitant northward shifts in the distribution of small pelagic species were attributed to long-term warming trends in SST and a decrease in marine productivity in the south. The abundance of Sardinella aurita, the most abundant species along the coast, has increased in the subtropics and fallen in the intertropical region. Spatial shifts in biomass were observed for other exploited small pelagic species, similar to those recorded for surface isotherms. An intensification in upwelling intensity within the northern and central regions of the system is documented without a change in marine primary productivity. In contrast, upwelling intensity is stable in the southern region, while there is a decline in primary productivity. These environmental differences affected several small pelagic species across national boundaries. This adds a new threat to these recently overexploited fish stocks, making sustainable management more difficult. Such changes must motivate common regional policy considerations for food security and sovereignty in all West African countries sharing the same stocks., (© 2024. The Author(s).)

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., (© 2022. The Author(s).)

Instrumental records suggest multidecadal variability in Arctic surface temperature throughout the twentieth century. This variability is caused by a combination of external forcing and internal variability, but their relative importance remains unclear. Since the early twentieth century Arctic warming has been linked to decadal variability in the Pacific, we hypothesize that the Pacific could impact decadal temperature trends in the Arctic throughout the twentieth century. To investigate this, we compare two ensembles of historical all-forcing twentieth century simulations with the Norwegian Earth System Model (NorESM): (1) a fully coupled ensemble and (2) an ensemble where momentum flux anomalies from reanalysis are prescribed over the Indo-Pacific Ocean to constrain Pacific sea surface temperature variability. We find that the combined effect of tropical and extratropical Pacific decadal variability can explain up to ~ 50% of the observed decadal surface temperature trends in the Arctic. The Pacific-Arctic connection involves both lower tropospheric horizontal advection and subsidence-induced adiabatic heating, mediated by Aleutian Low variations. This link is detected across the twentieth century, but the response in Arctic surface temperature is moderated by external forcing and surface feedbacks. Our results also indicate that increased ocean heat transport from the Atlantic to the Arctic could have compensated for the impact of a cooling Pacific at the turn of the twenty-first century. These results have implications for understanding the present Arctic warming and future climate variations., Competing Interests: Conflict of interestThe authors declare no conflict of interest., (© The Author(s) 2021.)

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change 1-3 . Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain 4 . This leads to low confidence in regional projections, especially for precipitation, over the coming decades 5,6 . The chaotic nature of the climate system 7-9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models 10 , and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade.

Interbasin interactions have been increasingly emphasized in recent years due to their roles in shaping climate trends and the global warming hiatus in the northern hemisphere. The profound influence from the North Atlantic on the Tropical Pacific has been a primary focus. In this study, we conducted observational analyses and numerical modeling experiments to show that the North Atlantic has also strongly influenced the Extratropical North Pacific. A rapid and synchronous change in the atmospheric and oceanic circulations was observed in the North Pacific during the late 1990s. The change was driven by the transbasin influence from the Atlantic Ocean. During the positive phase of the Atlantic Multidecadal Oscillation (AMO) since the 1990s, the anomalously warm North Atlantic triggers a series of zonally symmetric and asymmetric transbasin teleconnections involving the Inter-tropical Convergence Zone (ITCZ), Walker and Hadley circulations, and Rossby wave propagation that lead to a decrease in wind stress curls over the Pacific subtropics, resulting in an abrupt weakening in the North Pacific subtropical gyre (NPSG) and the Kuroshio Current.

Sea surface temperature variability in the equatorial eastern Atlantic, which is referred to as an Atlantic Niño (Niña) at its warm (cold) phase and peaks in boreal summer, dominates the interannual variability in the equatorial Atlantic. By strengthening of the Walker circulation, an Atlantic Niño favors a Pacific La Niña, which matures in boreal winter, providing a precursory memory for El Niño-Southern Oscillation (ENSO) predictability. How this Atlantic impact responds to greenhouse warming is unclear. Here, we show that greenhouse warming leads to a weakened influence from the Atlantic Niño/Niña on the Pacific ENSO. In response to anomalous equatorial Atlantic heating, ascending over the equatorial Atlantic is weaker due to an increased tropospheric stability in the mean climate, resulting in a weaker impact on the Pacific Ocean. Thus, as greenhouse warming continues, Pacific ENSO is projected to be less affected by the Atlantic Niño/Niña and more challenging to predict.

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., (Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

The individual impact of North Atlantic and Pacific Ocean Western Boundary Currents (OWBCs) on the tropospheric circulation has recently been studied in depth. However, their simultaneous role in shaping the hemisphere-scale wintertime troposphere/stratosphere-coupled circulation and its variability have not been considered. Through semi-idealized Atmospheric General-Circulation-Model experiments, we show that the North Atlantic and Pacific OWBCs jointly maintain and shape the wintertime hemispheric circulation and its leading mode of variability Northern Annular Mode (NAM). The OWBCs energize baroclinic waves that reinforce quasi-annular hemispheric structure in the tropospheric eddy-driven jetstreams and NAM variability. Without the OWBCs, the wintertime NAM variability is much weaker and its impact on the continental and maritime surface climate is largely insignificant. Atmospheric energy redistribution caused by the OWBCs acts to damp the near-surface atmospheric baroclinicity and compensates the associated oceanic meridional energy transport. Furthermore, the OWBCs substantially weaken the wintertime stratospheric polar vortex by enhancing the upward planetary wave propagation, and thereby affecting both stratospheric and tropospheric NAM-annularity. Whereas the overall impact of the extra-tropical OWBCs on the stratosphere results mainly from the Pacific, the impact on the troposphere results from both the Pacific and Atlantic OWBCs.

The supermodel strategy interactively combines several models to outperform the individual models comprising it. A key advantage of the approach is that nonlinear improvements can be achieved, in contrast to the linear weighted combination of individual unconnected models. This property is found in a climate supermodel constructed by coupling two versions of an atmospheric model differing only in their convection scheme to a single ocean model. The ocean model receives a weighted combination of the momentum and heat fluxes. Optimal weights can produce a supermodel with a basic state similar to observations: a single Intertropical Convergence zone (ITCZ), with a western Pacific warm pool and an equatorial cold tongue. This is in stark contrast to the erroneous double ITCZ pattern simulated by both of the two stand-alone coupled models. By varying weights, we develop a conceptual scheme to explain how combining the momentum fluxes of the two different atmospheric models affects equatorial upwelling and surface wind feedback so as to give a realistic basic state in the tropical Pacific. In particular, we propose a mechanism based on the competing influences of equatorial zonal wind and off-equatorial wind stress curl in driving equatorial upwelling in the coupled models. Our results show how nonlinear ocean-atmosphere interaction is essential in combining these two effects to build different sea surface temperature structures, some of which are realistic. They also provide some insight into observed and modelled tropical Pacific climate.

Decadal climate predictions use initialized coupled model simulations that are typically affected by a drift toward a biased climatology determined by systematic model errors. Model drifts thus reflect a fundamental source of uncertainty in decadal climate predictions. However, their analysis has so far relied on ad-hoc assessments of empirical and subjective character. Here, we define the climate model drift as a dynamical process rather than a descriptive diagnostic. A unified statistical Bayesian framework is proposed where a state-space model is used to decompose systematic decadal climate prediction errors into an initial drift, seasonally varying climatological biases and additional effects of co-varying climate processes. An application to tropical and south Atlantic sea-surface temperatures illustrates how the method allows to evaluate and elucidate dynamic interdependencies between drift, biases, hindcast residuals and background climate. Our approach thus offers a methodology for objective, quantitative and explanatory error estimation in climate predictions.

Nearly 50 years ago Bjerknes suggested that the character of large-scale air-sea interaction over the mid-latitude North Atlantic Ocean differs with timescales: the atmosphere was thought to drive directly most short-term--interannual--sea surface temperature (SST) variability, and the ocean to contribute significantly to long-term--multidecadal--SST and potentially atmospheric variability. Although the conjecture for short timescales is well accepted, understanding Atlantic multidecadal variability (AMV) of SST remains a challenge as a result of limited ocean observations. AMV is nonetheless of major socio-economic importance because it is linked to important climate phenomena such as Atlantic hurricane activity and Sahel rainfall, and it hinders the detection of anthropogenic signals in the North Atlantic sector. Direct evidence of the oceanic influence of AMV can only be provided by surface heat fluxes, the language of ocean-atmosphere communication. Here we provide observational evidence that in the mid-latitude North Atlantic and on timescales longer than 10 years, surface turbulent heat fluxes are indeed driven by the ocean and may force the atmosphere, whereas on shorter timescales the converse is true, thereby confirming the Bjerknes conjecture. This result, although strongest in boreal winter, is found in all seasons. Our findings suggest that the predictability of mid-latitude North Atlantic air-sea interaction could extend beyond the ocean to the climate of surrounding continents.