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The skilful prediction of climatic conditions on a forecast horizon of months to decades into the future remains a main scientific challenge of large societal benefit. Here we assess the hindcast skill of the Norwegian Climate Prediction Model (NorCPM) – for sea surface temperature (SST) and sea surface salinity (SSS) in the Arctic-Atlantic region – focusing on the impact of different initialization methods. We find the skill to be distinctly larger for the Subpolar North Atlantic than for the Norwegian Sea, and generally for all lead years analyzed. For the Subpolar North Atlantic, there is furthermore consistent benefit in increasing the amount of data assimilated, and also in updating the sea ice based on SST with strongly coupled data assimilation. The predictive skill is furthermore significant for at least two model versions up to 8-10 lead years with the exception for SSS at the longer lead years. For the Norwegian Sea, significant predictive skill is more rare; there is relatively higher skill with respect to SSS than for SST. A systematic benefit from more complex data assimilation approach can not be identified for this region. Somewhat surprisingly, skill deteriorates quite consistently for both the Subpolar North Atlantic and the Norwegian Sea when going from CMIP5 to corresponding CMIP6 versions. We find this to relate to change in the regional performance of the underlying physical model that dominates the benefit from initialization.

Society's need for operational climate forecast on seasonal to decadal time scales means an increased effort to improve climate prediction models. One way to address this issue is to investigate how initialization techniques affect the predictive skill in these systems.Considering this, three implementations and two versions of the Norwegian Climate Prediction Model (NorCPM) are analyzed concerning the effects of different initialization methods on the predictive skill in the Arctic-Atlantic region from interannual to decadal time scales. We consider aspects as data assimilation (DA) in the surface vs subsurface, DA update of sea-ice, CMIP5 vs CMIP6 NorCPM versions, ensemble size, and initialization frequency. Besides that, a comparison between the predictive skill in the Norwegian Sea (NS) and the Subpolar North Atlantic Ocean (SPNA) is performed to identify characteristics that can help to improve predictions in these areas.The additional assimilation of subsurface data increases the predictive skill in the SPNA at all lead times (1-10 years). In contrast, in the NS the skill is increased just at medium lead times (4-7 years). The strongly coupled DA, updating both ocean and sea ice, increases the predictive skill in the SPNA at all lead times, whereas the weakly coupled DA method, only updating ocean, results in higher skill in the NS at shorter (1-3 years) and medium (4-7 years) lead times. With respect to the NorCPM versions, the CMIP5 versions show higher predictive skill in both areas than the CMIP6 ones. In this comparison, besides the differences in the climate forcings, the new NorCPM version contributing to CMIP6 has minor code modifications, addition of interactive aerosol-cloud schemes, and an ocean component with biogeochemistry. Because of that, it is not possible to isolate just the effect of the climate forcings on the skill. Regarding the ensemble size and initialization frequency, NorCPM had a non-linear response; the skill varies with the area, variable, and lead times.Considering the results, no single version was superior to the others with respect to the skill. In the SPNA, the CMIP5 version, assimilating both surface and subsurface observations, and using strongly coupled DA, shows the highest skill. In the NS, we find the similar except that the highest skill is shown for the weakly coupled DA. Further investigation about technical aspects and the representation of dynamical process are necessary to better understand why the sea ice updating in the strongly coupled method is not beneficial to the NS.

The Arctic sea ice cover is currently retreating and will continue its retreat in a warming world. However, the loss of sea ice is neither regionally nor seasonally uniform. Here, we present the first regional and seasonal assessment of future Arctic sea ice loss in CMIP6 models under low (SSP126) and high (SSP585) emission scenarios, thus spanning the range of future change. We find that Arctic sea ice loss—at present predominantly limited to the summer season—will under SSP585 take place in all regions and all months. The summer sea ice is lost in all the shelf seas regardless of emission scenario, whereas ice-free conditions in winter before the end of this century only occur in the Barents Sea. The seasonal transition to ice-free conditions is found to spread through the Atlantic and Pacific regions, with change starting in the Barents Sea and Chukchi Sea, respectively. publishedVersion

There has recently been a large focus on identifying the mechanisms responsible for Atlantic multidecadal variability (AMV). However, decadal-scale variability embedded within the AMV has received less attention, despite being a prominent feature of observed North Atlantic sea surface temperature (SST) and important for the climate of adjacent continents. These decadal fluctuations in the North Atlantic Ocean are also a key source of skill in decadal climate predictions. However, the mechanisms underlying decadal SST variability remain to be fully understood. This study isolates the mechanisms driving North Atlantic SST variability on decadal time scales using low-frequency component analysis, which identifies the spatial and temporal structure of low-frequency variability. Based on observations, large ensemble historical simulations and pre-industrial control simulations, we identify a decadal mode of atmosphere-ocean variability in the North Atlantic with a dominant time scale of 13-18 years. Large-scale atmospheric circulation anomalies drive SST anomalies both through contemporaneous air-sea heat fluxes and through delayed ocean circulation changes, the latter involving both the meridional overturning circulation and the horizontal gyre circulation. The decadal SST anomalies alter the atmospheric meridional temperature gradient, leading to a reversal of the initial atmospheric circulation anomaly. The time scale of variability is consistent with westward propagation of baroclinic Rossby waves across the subtropical North Atlantic. The temporal development and spatial pattern of observed decadal SST variability are consistent with the recent observed cooling in the subpolar North Atlantic. This strongly suggests that the recent cold anomaly in the subpolar North Atlantic is, in part, a result of decadal SST variability, and that we might expect it to become less pronounced over the next few years.

The inflow across the Iceland-Scotland Ridge determines the amount of heat supplied to the Nordic Seas from the subpolar North Atlantic (SPNA). Consequently, variable inflow properties and volume transport at the ridge influence marine ecosystems and sea ice extent further north. Here, we identify the upstream pathways of the Nordic Seas inflow, and assess the mechanisms responsible for interannual inflow variability. Using an eddy-permitting ocean model hindcast and a Lagrangian analysis tool, numerical particles are released at the ridge during 1986-2015 and tracked backward in time. We find an inflow that is well-mixed in terms of its properties, where 64% comes from the subtropics and 26% has a subpolar or Arctic origin. The local instantaneous response to the NAO is important for the overall transport of both subtropical and Arctic-origin waters at the ridge. In the years before reaching the ridge, the subtropical particles are influenced by atmospheric circulation anomalies in the gyre boundary region and over the SPNA, forcing shifts in the North Atlantic Current (NAC) and the subpolar front. An equatorward shifted NAC and westward shifted subpolar front correspond to a warmer, more saline inflow. Atmospheric circulation anomalies over the SPNA also affect the amount of Arctic-origin water re-routed from the Labrador Current toward the Nordic Seas. A high transport of Arctic-origin water is associated with a colder, fresher inflow across the Iceland-Scotland Ridge. The results thus demonstrate the importance of gyre dynamics and wind forcing in affecting the Nordic Seas inflow properties and volume transport.

Statistical relationships between climate variables are good source of seasonal predictability, but can we trust them to be valid in the future? In two recent papers, we investigated the stationarity of some well-known lagged relationships. The predictors were Arctic sea surface temperatures (SSTs) and sea ice cover during autumn, and the predictands were the North Atlantic Oscillation (NAO) and European temperature in winter. The reason for studying these variables was that in recent decades, reduced sea ice and above-normal SSTs in autumn have often preceded negative NAO conditions and cold temperatures in Northern Europe in the following winter. When we looked further back in time, however, we found that the relationships between SST/ice and NAO/temperatures have been highly changeable and sometimes even the complete opposite to that seen recently. One key finding was that, according to two 20th century reanalyses, the strength of the negative lagged correlation between Barents Sea SST anomalies in fall and European temperature anomalies in winter after 1979 is unprecedented since 1900. An analysis of hundreds of simulations from multiple climate models confirms that the relationships vary with time, just due to natural climate variability. This led us to question the causality and/or robustness of the links between the variables and to caution against indiscriminately predicting wintertime weather based on Arctic sea ice and SST anomalies.

Arctic sea ice extent and sea surface temperature (SST) anomalies have been shown to be skillful predictors of weather anomalies in the midlatitudes on the seasonal time scale. In particular, below-normal sea ice extent in the Barents Sea in fall has sometimes preceded cold winters in parts of Eurasia. Here we explore the potential for predicting seasonal surface air temperature (SAT) anomalies in Europe from seasonal SST anomalies in the Nordic seas throughout the year. First, we show that fall SST anomalies not just in the Barents Sea but also in the Norwegian Sea have the potential to predict wintertime SAT anomalies in Europe. Norwegian Sea SST anomalies in spring are also significant predictors of European SAT anomalies in summer. Second, we demonstrate that the potential for prediction is sensitive to trends in the data. In particular, the lagged correlation between Norwegian Sea SST anomalies in spring and European SAT anomalies in summer is considerably higher for raw data than linearly detrended data, largely due to warming SST and SAT trends in recent decades. Third, we show that the potential for prediction has not been stationary in time. One key result is that, according to two twentieth-century reanalyses, the strength of the negative lagged correlation between Barents Sea SST anomalies in fall and European SAT anomalies in winter after 1979 is unprecedented since 1900.

Arctic winter sea ice loss is most pronounced in the Barents Sea. Here we combine observations since 1850 with climate model simulations to examine the recent record low winter Barents Sea ice extent. We find that the present observed winter Barents Sea ice extent has been reduced to less than 1/3 of the pre-satellite mean and is lower than the minimum sea ice extent in all multi-century climate model control simulations assessed here. The current observed sea ice loss is furthermore unprecedented in the observational record and appears as an uncommon trend in the long control simulations. In a warming climate, projections from the large ensemble simulation with the Community Earth System Model show a winter ice-free Barents Sea for the first time within the time period 2061–2088. The large spread in projections of ice-free conditions highlights the importance of internal variability in driving recent and future sea ice loss.

During recent decades Arctic sea ice variability and retreat during winter have largely been a result of variable ocean heat transport (OHT). Here we use the Community Earth System Model (CESM) large ensemble simulation to disentangle internally and externally forced winter Arctic sea ice variability, and to assess to what extent future winter sea ice variability and trends are driven by Atlantic heat transport. We find that OHT into the Barents Sea has been, and is at present, a major source of internal Arctic winter sea ice variability and predictability. In a warming world (RCP8.5), OHT remains a good predictor of winter sea ice variability, although the relation weakens as the sea ice retreats beyond the Barents Sea. Warm Atlantic water gradually spreads downstream from the Barents Sea and farther into the Arctic Ocean, leading to a reduced sea ice cover and substantial changes in sea ice thickness. The future long-term increase in Atlantic heat transport is carried by warmer water as the current itself is found to weaken. The externally forced weakening of the Atlantic inflow to the Barents Sea is in contrast to a strengthening of the Nordic Seas circulation, and is thus not directly related to a slowdown of the Atlantic meridional overturning circulation (AMOC). The weakened Barents Sea inflow rather results from regional atmospheric circulation trends acting to change the relative strength of Atlantic water pathways into the Arctic. Internal OHT variability is associated with both upstream ocean circulation changes, including AMOC, and large-scale atmospheric circulation anomalies reminiscent of the Arctic Oscillation.

A potential for climate predictability is rooted in anomalous ocean heat transport and its consequent influence on the atmosphere above. Here the propagation, drivers, and atmospheric impact of heat anomalies within the northernmost limb of the Atlantic meridional overturning circulation are assessed using a multicentury climate model simulation. Consistent with observation-based inferences, simulated heat anomalies propagate from the eastern subpolar North Atlantic into and through the Nordic seas. The dominant time scale of associated climate variability in the northern seas is 14 years, including that of observed sea surface temperature and modeled ocean heat content, air–sea heat flux, and surface air temperature. A heat budget analysis reveals that simulated ocean heat content anomalies are driven by poleward ocean heat transport, primarily related to variable volume transport. The ocean’s influence on the atmosphere, and hence regional climate, is manifested in the model by anomalous ocean heat convergence driving subsequent changes in surface heat fluxes and surface air temperature. The documented northward propagation of thermohaline anomalies in the northern seas and their consequent imprint on the regional atmosphere—including the existence of a common decadal time scale of variability—detail a key aspect of eventual climate predictability.

A main concern of present climate change is the Arctic sea ice cover. In wintertime, its observed variability is largely carried by the Barents Sea. Here we propose and evaluate a simple quantitative and prognostic framework based on first principles and rooted in observations to predict the annual mean Barents Sea ice cover, which variance is carried by the winter ice (96%). By using observed ocean heat transport and sea ice area, the proposed framework appears skillful and explains 50% of the observed sea ice variance up to 2 years in advance. The qualitative prediction of increase versus decrease in ice cover is correct 88% of the time. Model imperfections can largely be diagnosed from simultaneous meridional winds. The framework and skill are supported by a 60 year simulation from a regional ice-ocean model. We particularly predict that the winter sea ice cover for 2016 will be slightly less than 2015.

Predicting fish stock variations on interannual to decadal time scales is one of the major issues in fisheries science and management. Although the field of marine ecological predictions is still in its infancy, it is understood that a major source of multi-year predictability resides in the ocean. Here we show the first highly skilful long-term predictions of the commercially valuable Barents Sea cod stock. The 7-year predictions are based on the propagation of ocean temperature anomalies from the subpolar North Atlantic toward the Barents Sea, and the strong co-variability between these temperature anomalies and the cod stock. Retrospective predictions for the period 1957–2017 capture well multi-year to decadal variations in cod stock biomass, with cross-validated explained variance of over 60%. For lead times longer than one year the statistical long-term predictions show more skill than operational short-term predictions used in fisheries management and lagged persistence forecasts. Our results thus demonstrate the potential for ecosystem-based fisheries management, which could enable strategic planning on longer time scales. Future predictions show a gradual decline in the cod stock towards 2024. publishedVersion

The recent Arctic winter sea ice retreat is most pronounced in the Barents Sea. Using available observations of the Atlantic inflow to the Barents Sea and results from a regional ice–ocean model the authors assess and quantify the role of inflowing heat anomalies on sea ice variability. The interannual variability and longer-term decrease in sea ice area reflect the variability of the Atlantic inflow, both in observations and model simulations. During the last decade (1998–2008) the reduction in annual (July–June) sea ice area was 218 × 103 km2, or close to 50%. This reduction has occurred concurrent with an increase in observed Atlantic heat transport due to both strengthening and warming of the inflow. Modeled interannual variations in sea ice area between 1948 and 2007 are associated with anomalous heat transport (r = −0.63) with a 70 × 103 km2 decrease per 10 TW input of heat. Based on the simulated ocean heat budget it is found that the heat transport into the western Barents Sea sets the boundary of the ice-free Atlantic domain and, hence, the sea ice extent. The regional heat content and heat loss to the atmosphere scale with the area of open ocean as a consequence. Recent sea ice loss is thus largely caused by an increasing “Atlantification” of the Barents Sea.

Nature Communications 8: Article number: 15875 (2017); Published 20 June 2017; Updated 22 December 2017 In Fig. 2 of the original Article, information indicating the extent of the lagged correlations between low-passed and detrended time series was inadvertently omitted during the production process. The correct version of this figure appears below as Fig.

A 40 year (1958–1997) hindcast simulation from the regional coupled ice–ocean model HAMSOM is used to study climate relevant processes in the Barents Sea and their interannual to decadal variability. Compared to observations the model captures the variability in temperature and ice extent in a satisfying manner. The heat input through the Barents Sea Opening (BSO) is effectively lost through intense atmosphere–ocean heat exchange within the Barents Sea. Correlation analysis suggests that heat transport through the BSO leads the Barents Sea heat content by 1–10 months, while the heat content leads the air–sea heat fluxes with 1–5 months. Averaged over the period the advected heat input is 32 TW, augmented by 79 TW of shortwave radiation and reduced by 113 TW through longwave radiation and latent and sensible heat loss. Including the sensible heat loss at the ice–ocean boundary yields an oceanic heat loss in the Barents Sea of 40 TW. Cooling of Atlantic Water is very efficient just east of the BSO, and contributes to 50% of the total heat loss. Significant positive trends in both heat transport through the BSO and solar radiation, combined with a reduction in seasonal ice cover cause increased oceanic heat loss. Excess heat still enters the Barents Sea and a significant warming is observed in the northern areas. Sea-ice acts as an effective insulator against oceanic heat loss resulting in 4 TW of net heat input at the sea-ice surface. This heat flux balances the ice–ocean heat budget and the corresponding ice melt compensates for net ice production at the ice–ocean interface and ice advection into the Barents Sea.

[1] To capture the austral summer to winter transition in water mass properties over the southern Weddell Sea continental shelf and slope region, 19 Weddell seals were tagged with miniaturized conductivity–temperature–depth sensors in February 2011. During the following 8 months the instruments yielded about 9000 temperature–salinity profiles from a previously undersampled area. This allows, for the first time, a description of the seasonality of warm water intrusions onto the shelf, as well as its southward extent towards the Filchner Ice Shelf. A temperature section across the Filchner Depression and eastern shelf shows a pronounced decrease in warm water inflow from summer to winter, further supported by an almost 3–year long time series from a shelf–break mooring. The seasonal variability is related to the surface wind stress and an associated deepening of the off-shelf core of Warm Deep Water.

The growing attention on mechanisms that can provide predictability on interannual-to-decadal time scales, makes it necessary to identify how well climate models represent such mechanisms. In this study we use a high (0.25° horizontal grid) and a medium (1°) resolution version of a forced global ocean-sea ice model, utilising the Norwegian Earth System Model, to assess the impact of increased ocean resolution. Our target is the simulation of temperature and salinity anomalies along the pathway of warm Atlantic water in the subpolar North Atlantic and the Nordic Seas. Although the high resolution version has larger biases in general at the ocean surface, the poleward propagation of thermohaline anomalies is better resolved in this version, i.e., the time for an anomaly to travel northward is more similar to observation based estimates. The extent of these anomalies can be rather large in both model versions, as also seen in observations, e.g., stretching from Scotland to northern Norway. The easternmost branch into the Nordic and Barents Seas, carrying warm Atlantic water, is also improved by higher resolution, both in terms of mean heat transport and variability in thermohaline properties. A more detailed assessment of the link between the North Atlantic Ocean circulation and the thermohaline anomalies at the entrance of the Nordic Seas reveals that the high resolution is more consistent with mechanisms that are previously published. This suggests better dynamics and variability in the subpolar region and the Nordic Seas in the high resolution compared to the medium resolution. This is most likely due a better representation of the mean circulation in the studied region when using higher resolution. As the poleward propagation of ocean heat anomalies is considered to be a key source of climate predictability, we recommend that similar methodology presented herein should be performed oncoupledclimate models that are used for climate prediction.