Bjerknes Climate Prediction Unit

Årthun, M., Asbjørnsen, H., Chafik, L., Johnson, H.L., Våge, K. 2023: Future strengthening of the Nordic Seas overturning circulation. Nat Commun. https://www.nature.com/articles/s41467-023-37846-6 Summary: The overturning circulation in the Nordic Seas involves the transformation of warm Atlantic waters into cold, dense overflows. These overflow waters return to the North Atlantic and form the headwaters to the deep limb of the Atlantic meridional overturning circulation (AMOC). The Nordic Seas are thus a key component of the AMOC. However, little is known about the response of the overturning circulation in the Nordic Seas to future climate change. Here we show using global climate models that, in contrast to the North Atlantic, the simulated density-space overturning circulation in the Nordic Seas increases throughout most of the 21st century as a result of enhanced horizontal circulation and a strengthened zonal density gradient. The increased Nordic Seas overturning is furthermore manifested in the overturning circulation in the eastern subpolar North Atlantic. A strengthened Nordic Seas overturning circulation could therefore be a stabilizing factor in the future AMOC. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Rieke, O., Årthun, M., Dörr, J.S. 2023: Rapid sea ice changes in the future Barents Sea. The Cryosphere. https://doi.org/10.5194/tc-17-1445-2023 Summary: Observed and future winter Arctic sea ice loss is strongest in the Barents Sea. However, the anthropogenic signal of the sea ice decline is superimposed by pronounced internal variability that represents a large source of uncertainty in future climate projections. A notable manifestation of internal variability is rapid ice change events (RICEs) that greatly exceed the anthropogenic trend. These RICEs are associated with large displacements of the sea ice edge which could potentially have both local and remote impacts on the climate system. In this study we present the first investigation of the frequency and drivers of RICEs in the future Barents Sea, using multi-member ensemble simulations from CMIP5 and CMIP6. A majority of RICEs are triggered by trends in ocean heat transport or surface heat fluxes. Ice loss events are associated with increasing trends in ocean heat transport and decreasing trends in surface heat loss. RICEs are a common feature of the future Barents Sea until the region becomes close to ice-free. As their evolution over time is closely tied to the average sea ice conditions, rapid ice changes in the Barents Sea may serve as a precursor for future changes in adjacent seas. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Counillon, F., Keenlyside, N., Wang, S., Devilliers, M., Gupta, A., Koseki, S., Shen, M.-L. 2023: Framework for an Ocean-Connected Supermodel of the Earth System. JAMES. https://doi.org/10.1029/2022MS003310 Summary: Observed and future winter Arctic sea ice loss is strongest in the Barents Sea. However, the anthropogenic signal of the sea ice decline is superimposed by pronounced internal variability that represents a large source of uncertainty in future climate projections. A notable manifestation of internal variability is rapid ice change events (RICEs) that greatly exceed the anthropogenic trend. These RICEs are associated with large displacements of the sea ice edge which could potentially have both local and remote impacts on the climate system. In this study we present the first investigation of the frequency and drivers of RICEs in the future Barents Sea, using multi-member ensemble simulations from CMIP5 and CMIP6. A majority of RICEs are triggered by trends in ocean heat transport or surface heat fluxes. Ice loss events are associated with increasing trends in ocean heat transport and decreasing trends in surface heat loss. RICEs are a common feature of the future Barents Sea until the region becomes close to ice-free. As their evolution over time is closely tied to the average sea ice conditions, rapid ice changes in the Barents Sea may serve as a precursor for future changes in adjacent seas. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Kjesbu, O.S., Alix, M., Sandø, A.B., Strand, E., Wright, P.J., Johns, D.G., Thorsen, A., Marshall, C.T., Bakkeplass, K.G., Vikebø, F.B., Myksvoll, M.S., Ottersen, G., Allan, B.J.M., Fossheim, M., Stiansen, J.E., Huse, G., Sundby, S. 2023: Latitudinally distinct stocks of Atlantic cod face fundamentally different biophysical challenges under on-going climate change. Fish and Fisheries. https://doi.org/10.1111/faf.12728 Summary: Observed and future winter Arctic sea ice loss is strongest in the Barents Sea. However, the anthropogenic signal of the sea ice decline is superimposed by pronounced internal variability that represents a large source of uncertainty in future climate projections. A notable manifestation of internal variability is rapid ice change events (RICEs) that greatly exceed the anthropogenic trend. These RICEs are associated with large displacements of the sea ice edge which could potentially have both local and remote impacts on the climate system. In this study we present the first investigation of the frequency and drivers of RICEs in the future Barents Sea, using multi-member ensemble simulations from CMIP5 and CMIP6. A majority of RICEs are triggered by trends in ocean heat transport or surface heat fluxes. Ice loss events are associated with increasing trends in ocean heat transport and decreasing trends in surface heat loss. RICEs are a common feature of the future Barents Sea until the region becomes close to ice-free. As their evolution over time is closely tied to the average sea ice conditions, rapid ice changes in the Barents Sea may serve as a precursor for future changes in adjacent seas. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Kvamsdal, S.F., Dankel, D., Ekerhovd, N.-A., Hoel, A.H., Renner, A., Sandø, A.B., Steinshamn, S.I. 2022: Multidisciplinary perspectives on living marine resources in the Arctic. Polar Research. https://doi.org/10.33265/polar.v41.7766 Summary: Many areas in the Arctic are vulnerable to the impacts of climate change. We observe large-scale effects on physical, biological, economic and social parameters, including ice cover, species distributions, economic activity and regional governance frameworks. Arctic living marine resources are affected in various ways. A holistic understanding of these effects requires a multidisciplinary enterprise. We synthesize relevant research, from oceanography and ecology, via economics, to political science and international law. We find that multidisciplinary research can enhance our understanding and promote new questions and issues relating to impacts and outcomes of climate change in the Arctic. Such issues include recent insights on changing spawning migrations of the North-east Arctic cod stock that necessitates revisions of socioeconomic estimates of ecosystem wealth in the Barents Sea, better integrated prediction systems that require increased cooperation between experts on climate prediction and ecosystem modelling, and institutional complexities of Arctic governance that require enhanced coordination. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Crespo, L.R., Prigent, A., Keenlyside, N., Koseki, S., Svendsen, L., Richter, I., Sánchez-Gómez, E. 2022: Weakening of the Atlantic Niño variability under global warming. Nat. Clim. Chang. https://doi.org/10.1038/s41558-022-01453-y Summary: The Atlantic Niño is one of the most important patterns of interannual tropical climate variability, but how climate change will influence this pattern is not well known due to large climate model biases. Here we show that state-of-the-art climate models robustly predict a weakening of Atlantic Niños in response to global warming, mainly due to a decoupling of subsurface and surface temperature variations as the upper equatorial Atlantic Ocean warms. This weakening is predicted by most (>80%) models in the Coupled Model Intercomparison Project Phases 5 and 6 under the highest emission scenarios. Our results indicate a reduction in variability by the end of the century by 14%, and as much as 24–48% when accounting for model errors using a simple emergent constraint analysis. Such a weakening of Atlantic Niño variability will potentially impact climate conditions and the skill of seasonal predictions in many regions. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Barthélémy, S., Brajard, J., Bertino, L., Counillon, F. 2022: Super-resolution data assimilation. Ocean Dyn. https://doi.org/10.1007/s10236-022-01523-x Summary: Increasing model resolution can improve the performance of a data assimilation system because it reduces model error, the system can more optimally use high-resolution observations, and with an ensemble data assimilation method the forecast error covariances are improved. However, increasing the resolution scales with a cubical increase of the computational costs. A method that can more effectively improve performance is introduced here. The novel approach called “Super-resolution data assimilation” (SRDA) is inspired from super-resolution image processing techniques and brought to the data assimilation context. Starting from a low-resolution forecast, a neural network (NN) emulates the fields to high-resolution, assimilates high-resolution observations, and scales it back up to the original resolution for running the next model step. The SRDA is tested with a quasi-geostrophic model in an idealized twin experiment for configurations where the model resolution is twice and four times lower than the reference solution from which pseudo-observations are extracted. The assimilation is performed with an Ensemble Kalman Filter. We show that SRDA outperforms both the low-resolution data assimilation approach and a version of SRDA with cubic spline interpolation instead of NN. The NN’s ability to anticipate the systematic differences between low- and high-resolution model dynamics explains the enhanced performance, in particular by correcting the difference of propagation speed of eddies. With a 25-member ensemble at low resolution, the SRDA computational overhead is 55 percent and the errors reduce by 40 percent, making the performance very close to that of the high-resolution system (52 percent of error reduction) that increases the cost by 800 percent. The reliability of the ensemble system is not degraded by SRDA. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Passos, L., Langehaug, HR., Årthun, M., Eldevik, T., Bethke, I., Kimmritz, M. 2022: Impact of initialization methods on the predictive skill in NorCPM: an Arctic–Atlantic case study. Clim Dyn. https://doi.org/10.1007/s00382-022-06437-4 Summary: 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. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Athanasiadis, P.J., Ogawa, F., Omrani, N.-E., Keenlyside, N., Schiemann, R., Baker, A.J., Vidale, P.L., Bellucci, A., Ruggieri, P., Haarsma, R., Roberts, M., Roberts, C., Novak, L., Gualdi, S. 2022: Mitigating climate biases in the mid-latitude North Atlantic by increasing model resolution: SST gradients and their relation to blocking and the jet. J Clim. https://doi.org/10.1007/s10236-022-01523-x Summary: Starting to resolve the oceanic mesoscale in climate models is a step change in model fidelity. This study examines how certain obstinate biases in the midlatitude North Atlantic respond to increasing resolution (from 1° to 0.25° in the ocean) and how such biases in sea surface temperature (SST) affect the atmosphere. Using a multi-model ensemble of historical climate simulations run at different horizontal resolutions, it is shown that a severe cold SST bias in the central North Atlantic, common to many ocean models, is significantly reduced with increasing resolution. The associated bias in the time-mean meridional SST gradient is shown to relate to a positive bias in low-level baroclinicity, while the cold SST bias causes biases also in static stability and diabatic heating in the interior of the atmosphere. The changes in baroclinicity and diabatic heating brought by increasing resolution lead to improvements in European blocking and eddy-driven jet variability. Across the multi-model ensemble a clear relationship is found between the climatological meridional SST gradients in the broader Gulf Stream Extension area and two aspects of the atmospheric circulation: the frequency of high-latitude blocking and the southern-jet regime. This relationship is thought to reflect the two-way interaction (with a positive feedback) between the respective oceanic and atmospheric anomalies. These North Atlantic SST anomalies are shown to be important in forcing significant responses in the midlatitude atmospheric circulation, including jet variability and the stormtrack. Further increases in oceanic and atmospheric resolution are expected to lead to additional improvements in the representation of Euro-Atlantic climate. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Omrani, NE., Keenlyside, N., Matthes, K., Boljka, L., Zanchettin, D., Jungclaus, JH., Lubis, SW. 2022: Coupled stratosphere-troposphere-Atlantic multidecadal oscillation and its importance for near-future climate projection. npj Clim Atmos Sci. https://doi.org/10.1038/s41612-022-00275-1 Summary: Northern Hemisphere (NH) climate has experienced various coherent wintertime multidecadal climate trends in stratosphere, troposphere, ocean, and cryosphere. However, the overall mechanistic framework linking these trends is not well established. Here we show, using long-term transient forced coupled climate simulation, that large parts of the coherent NH-multidecadal changes can be understood within a damped coupled stratosphere/troposphere/ocean-oscillation framework. Wave-induced downward propagating positive stratosphere/troposphere-coupled Northern Annular Mode (NAM) and associated stratospheric cooling initiate delayed thermohaline strengthening of Atlantic overturning circulation and extratropical Atlantic-gyres. These increase the poleward oceanic heat transport leading to Arctic sea-ice melting, Arctic warming amplification, and large-scale Atlantic warming, which in turn initiates wave-induced downward propagating negative NAM and stratospheric warming and therefore reverse the oscillation phase. This coupled variability improves the performance of statistical models, which project further weakening of North Atlantic Oscillation, North Atlantic cooling and hiatus in wintertime North Atlantic-Arctic sea-ice and global surface temperature just like the 1950s–1970s. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Zaplotnik, Ž., M. Pikovnik, L. Boljka, L. 2022: Recent Hadley Circulation Strengthening: A Trend or Multidecadal Variability? J Clim. https://doi.org/10.1175/JCLI-D-21-0204.1 Summary: This study explores the possible drivers of the recent Hadley circulation strengthening in the modern reanalyses. Predominantly, two recent generations of reanalyses provided by the European Centre for Medium-Range Weather Forecasts are used: the fifth-generation atmospheric reanalysis (ERA5) and the interim reanalysis (ERA-Interim). Some results are also evaluated against other long-term reanalyses. To assess the origins of the Hadley cell (HC) strength variability, we employ the Kuo–Eliassen (KE) equation. ERA5 shows that both HCs were strengthening prior to the 2000s, but they have been weakening or remained steady afterward. Most of the long-term variability in the strength of the HCs is explained by the meridional gradient of diabatic (latent) heating, which is related to precipitation gradients. However, the strengthening of both HCs in ERA5 is larger than the strengthening expected from the observed zonal-mean precipitation gradient [estimated from the Global Precipitation Climatology Project (GPCP)]. This suggests that the HC strength trends in the recent decades in ERA5 can be explained partly as an artifact of the misrepresentation of latent heating and partly through (physical) long-term variability. To show that the latter is true, we analyze ERA5 preliminary data for the 1950–78 period, other long-term (e.g., twentieth century) reanalyses, and sea surface temperature observational data. This reveals that the changes in the HC strength can be a consequence of the Atlantic multidecadal oscillation (AMO) and related diabatic and frictional processes, which in turn drive the global HC variability. This work has implications for further understanding of the long-term variability of the Hadley circulation. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Pikovnik, M., Zaplotnik, Ž., Boljka, L., Žagar, N. 2022: Metrics of the Hadley circulation strength and associated circulation trends. Weather Clim Dynam. https://doi.org/10.5194/wcd-3-625-2022 Summary: This study compares trends in the Hadley cell (HC) strength using different metrics applied to the ECMWF ERA5 and ERA-Interim reanalyses for the period 1979–2018. The HC strength is commonly evaluated by metrics derived from the mass-weighted zonal-mean stream function in isobaric coordinates. Other metrics include the upper tropospheric velocity potential, the vertical velocity in the mid-troposphere, and the water vapour transport in the lower troposphere. Seven known metrics of HC strength are complemented here by a metric of the spatially averaged HC strength, obtained by averaging the stream function in the latitude–pressure (φ–p) plane, and by the total energy of zonal-mean unbalanced circulation in the normal-mode function decomposition. It is shown that metrics, which rely on single-point values in the φ–p plane, produce unreliable 40-year trends in both the northern and southern HCs, especially in ERA-Interim; magnitudes and even the signs of the trends depend on the choice of the HC strength metric. The two new metrics alleviate the vertical and meridional inhomogeneities of the trends in HC strength. The unbalanced energy metric suggests a positive HC trend in both reanalyses, whereas the metric based on averaging the stream function finds a significant positive trend only in ERA5. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Payne, M.R., Danabasoglu, G., Keenlyside, N., Matei, D., Miesner, A.K., Yang, S., Yeager, S.G. 2022: Skilful decadal-scale prediction of fish habitat and distribution shifts. Nat. Commun. https://doi.org/10.1038/s41467-022-30280-0 Summary: 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. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Schevenhoven, F., Carrassi, A. 2022: Training a supermodel with noisy and sparse observations: a case study with CPT and the synch rule on SPEEDO – v.1. Geosci. Model Dev. https://doi.org/10.5194/gmd-15-3831-2022 Summary: As an alternative to using the standard multi-model ensemble (MME) approach to combine the output of different models to improve prediction skill, models can also be combined dynamically to form a so-called supermodel. The supermodel approach enables a quicker correction of the model errors. In this study we connect different versions of SPEEDO, a global atmosphere-ocean-land model of intermediate complexity, into a supermodel. We focus on a weighted supermodel, in which the supermodel state is a weighted superposition of different imperfect model states. The estimation, “the training”, of the optimal weights of this combination is a critical aspect in the construction of a supermodel. In our previous works two algorithms were developed: (i) cross pollination in time (CPT)-based technique and (ii) a synchronization-based learning rule (synch rule). Those algorithms have so far been applied under the assumption of complete and noise-free observations. Here we go beyond and consider the more realistic case of noisy data that do not cover the full system’s state and are not taken at each model’s computational time step. We revise the training methods to cope with this observational scenario, while still being able to estimate accurate weights. In the synch rule an additional term is introduced to maintain physical balances, while in CPT nudging terms are added to let the models stay closer to the observations during training. Furthermore, we propose a novel formulation of the CPT method allowing the weights to be negative. This makes it possible for CPT to deal with cases in which the individual model biases have the same sign, a situation that hampers constructing a skillfully weighted supermodel based on positive weights. With these developments, both CPT and the synch rule have been made suitable to train a supermodel consisting of state of the art weather and climate models. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Hermanson, L., Smith, D., Seabrook, M., Bilbao, R., Doblas-Reyes, F., Tourigny, E., Lapin, V., Kharin, V.V., Merryfield, W.J., Sospedra-Alfonso, R., Athanasiadis, P., Nicoli, D., Gualdi, S., Dunstone, N., Eade, R., Scaife, A., Collier, M., O’Kane, T., Kitsios, V., Sandery, P., Pankatz, K., Früh, B., Pohlmann, H., Müller, W., Kataoka, T., Tatebe, H., Ishii M., Imada, Y., Kruschke, T., Koenigk, T., Pasha Karami, M., Yang, S., Tian, T., Zhang, L., Delworth, T., Yang, X., Zeng, F., Wang, Y., Counillon, F., Keenlyside, N.S., Bethke, I., Lean, J., Luterbacher, J., Kumar Kolli, R., Kumar, A. 2022: WMO Global Annual to Decadal Climate Update: A Prediction for 2021–25. BAMS https://doi.org/10.1175/BAMS-D-20-0311.1 . Summary: As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future.. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Langehaug, H. R., Ortega, P., Counillon, F., Matei, D., Maroon, E., Keenlyside, N., Mignot, J., Wang, Y., Swingedouw, D., Bethke, I., Yang, S., Danabasoglu, G., Bellucci, A., Ruggieri, P., Nicolì, D., Årthun, M. 2022: Propagation of Thermohaline Anomalies and Their Predictive Potential along the Atlantic Water Pathway. J Clim. https://doi.org/10.1007/s10236-022-01523-x Summary: In this study, we find that dynamical prediction systems and their respective climate models struggle to realistically represent ocean surface temperature variability in the eastern subpolar North Atlantic and Nordic seas on interannual-to-decadal time scales. In previous studies, ocean advection is proposed as a key mechanism in propagating temperature anomalies along the Atlantic water pathway toward the Arctic Ocean. Our analysis suggests that the predicted temperature anomalies are not properly circulated to the north; this is a result of model errors that seems to be exacerbated by the effect of initialization shocks and forecast drift. Better climate predictions in the study region will thus require improving the initialization step, as well as enhancing process representation in the climate models. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Efstathiou, E., Eldevik, T., Årthun, M., Lind, S. 2022: Spatial patterns, mechanisms and predictability of Barents Sea ice change. J Clim. https://doi.org/10.1175/JCLI-D-21-0044.1  . Summary: Recent Arctic winter sea ice loss has been most pronounced in the Barents Sea. Here we explore the spatial structure of Barents Sea ice change as observed over the last 40 years. The dominant mode of winter sea ice concentration interannual variability corresponds to areal change (explains 43% of spatial variance) and has a center of action in the northeastern Barents Sea where the temperate Atlantic inflow meets the wintertime sea-ice. Sea ice area import and northerly wind also contribute to this “areal-change mode”; the area increases with more ice import and stronger winds from the north. The remaining 57% variance in sea ice, individually and combined, redistributes the sea ice without changing the total area. The two leading redistribution modes are a dipole of increase in sea ice concentration south of Svalbard with decrease southwest of Novaya Zemlya, and a tripole of increase in the central Barents Sea with decrease east of Svalbard and in the southeastern Barents Sea. Redistribution is mainly contributed by anomalous wind and sea ice area import. Basic predictability, i.e., the lagged response to observed drivers, is predominantly associated with the areal-change mode as influenced by temperature of the Atlantic inflow and sea ice import from the Arctic. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Singh, T., Counillon, F., Tjiputra, J., Wang Y., El Gharamti, M. 2022: Estimation of Ocean Biogeochemical Parameters in an Earth System Model Using the Dual One Step Ahead Smoother: A Twin Experiment. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.775394 . For an easy-to-understand overview of this publication, produced in collaboration with the TRIATLAS project, we recommend starting with this neat article written by Henrike Wilborn, at NERSC: “Making climate models more accurate by improving their tuning”. Summary: Ocean biogeochemical (BGC) models utilise a large number of poorly-constrained global parameters to mimic unresolved processes and reproduce the observed complex spatio-temporal patterns. Large model errors stem primarily from inaccuracies in these parameters whose optimal values can vary both in space and time. This study aims to demonstrate the ability of ensemble data assimilation (DA) methods to provide high-quality and improved BGC parameters within an Earth system model in an idealized perfect twin experiment framework. We use the Norwegian Climate Prediction Model (NorCPM), which combines the Norwegian Earth System Model with the Dual-One-Step ahead smoothing-based Ensemble Kalman Filter (DOSA-EnKF). We aim to estimate five spatially varying BGC parameters by assimilating salinity and temperature profiles and surface BGC (Phytoplankton, Nitrate, Phosphate, Silicate, and Oxygen) observations in a strongly coupled DA framework—i.e., jointly updating ocean and BGC state-parameters during the assimilation. We show how BGC observations can effectively constrain error in the ocean physics and vice versa. The method converges quickly (less than a year) and largely reduces the errors in the BGC parameters. Some parameter error remains, but the resulting state variable error using the estimated parameters for a free ensemble run and for a reanalysis performs nearly as well as with true parameter values. Optimal parameter values can also be recovered by assimilating climatological BGC observations or sparse observational networks. The findings of this study demonstrate the applicability of the DA approach for tuning the system in a real framework. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Cheung, HN., Keenlyside, N., Koenigk, T., Yang, S., Tian, T., Xu, Z., Gao, Y., Ogawa, F., Omrani, N.-E., Qiao, S., Zhou, W. 2022: Assessing the influence of sea surface temperature and arctic sea ice cover on the uncertainty in the boreal winter future climate projections. Clim. Dyn. https://doi.org/10.1007/s00382-022-06136-0 Summary: We investigate the uncertainty (i.e., inter-model spread) in future projections of the boreal winter climate, based on the forced response of ten models from the CMIP5 following the RCP8.5 scenario. The uncertainty in the forced response of sea level pressure (SLP) is large in the North Pacific, the North Atlantic, and the Arctic. A major part of these uncertainties (31%) is marked by a pattern with a center in the northeastern Pacific and a dipole over the northeastern Atlantic that we label as the Pacific–Atlantic SLP uncertainty pattern (PA∆SLP). The PA∆SLP is associated with distinct global sea surface temperature (SST) and Arctic sea ice cover (SIC) perturbation patterns. To better understand the nature of the PA∆SLP, these SST and SIC perturbation patterns are prescribed in experiments with two atmospheric models (AGCMs): CAM4 and IFS. The AGCM responses suggest that the SST uncertainty contributes to the North Pacific SLP uncertainty in CMIP5 models, through tropical–midlatitude interactions and a forced Rossby wavetrain. The North Atlantic SLP uncertainty in CMIP5 models is better explained by the combined effect of SST and SIC uncertainties, partly related to a Rossby wavetrain from the Pacific and air-sea interaction over the North Atlantic. Major discrepancies between the CMIP5 and AGCM forced responses over northern high-latitudes and continental regions are indicative of uncertainties arising from the AGCMs. We analyze the possible dynamic mechanisms of these responses, and discuss the limitations of this work. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Hordoir, R., Skagseth, Ø., Ingvaldsen, R.B., Sandø, A.B., Löptien, U., Dietze, H., Gierisch, A.M.U., Assmann K.A., Lundesgaard,Ø., Lind, S. 2022: Changes in Arctic Stratification and Mixed Layer Depth Cycle: A Modeling Analysis. JGR Oceans. https://doi.org/10.1029/2021JC017270 Summary: We analyzed the results of an ocean model simulation for the Arctic and North Atlantic oceans for the period 1970–2019. Our model is in line with the recent observed changes in the Arctic Ocean and allows, in contrast to the rather sparse observations, a detailed assessment of stratification changes. These changes will affect the Arctic ecosystem and are also believed to affect the large scale ocean circulation. We show that major changes in upper ocean conditions are caused by changes in the fresh water supply by sea ice and varying effect of the wind on regions that are now becoming ice-free. We also study the effect of changes in river runoff into the Arctic Ocean. Our study shows that an increase in river runoff can change the coastal circulation and results, paradoxically, in regions of higher salinity. These results point to the importance of modeling tools when it comes to a better understanding of ocean processes in a changing climate. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Bethke, I., Wang, Y., Counillon, F., Keenlyside, N., Kimmritz, M., Fransner, F., Samuelsen, A., Langehaug, H., Svendsen, L., Chiu, P.-G., Passos, L., Bentsen, M., Guo, C., Gupta, A., Tjiputra, J., Kirkevåg, A., Olivié, D., Seland, Ø., Solsvik Vågane, J., Fan, Y., Eldevik, T. 2021: NorCPM1 and its contribution to CMIP6 DCPP. Geosci Model Dev. https://doi.org/10.5194/gmd-14-7073-2021 . For an easy-to-understand overview, we recommend starting with this neat article written by the Climate Futures team, a project connected to BCPU: “New Study: Decadal Climate Forecasts From The Norwegian Climate Prediction Model” (les heller på norsk). Summary: 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 -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. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Silva, E.F.F., Counillon, F., Brajard, J., Korosov, A., Pettersson, L., Samuelsen, A., Keenlyside, N. 2021: Twenty-one years of phytoplankton bloom phenology in the Barents, Norwegian and North seas. Front Mar Sci.  https://doi.org/10.3389/fmars.2021.746327 . For en flott oppsummering på norsk, les denne artikkelen av vår samarbeidspartner, Climate Futures. Summary: Phytoplankton blooms provide biomass to the marine trophic web, contribute to the carbon removal from the atmosphere and can be deadly when associated with harmful species. This points to the need to understand the phenology of the blooms in the Barents, Norwegian, and North seas. We use satellite chlorophyll-a from 2000 to 2020 to assess robust climatological and the interannual trends of spring and summer blooms onset, peak day, duration and intensity. Further, we also correlate the interannual variability of the blooms with mixed layer depth (MLD), sea surface temperature (SST), wind speed and suspended particulate matter (SPM) retrieved from models and remote sensing. The climatological spring blooms start on March 10th and end on June 19th. The climatological summer blooms begin on July 13th and end on September 17th. In the Barents Sea, years of shallower mixed layer (ML) driven by both calm waters and higher freshwaters input keeps the phytoplankton in the euphotic zone, causing the spring bloom to start earlier and reach higher biomass but end sooner due to the lack of nutrients upwelling from the deep. In the Norwegian Sea, a correlation between SST and the spring blooms is found. Here, warmer waters are correlated to earlier and stronger blooms in most regions but with later and weaker blooms in the eastern Norwegian Sea. In the North Sea, years of shallower ML reduces the phytoplankton sinking below the euphotic zone and limits the SPM increase from the bed shear stress, creating an ideal environment of stratified and clear waters to develop stronger spring blooms. Last, the summer blooms onset, peak day and duration have been rapidly delaying at a rate of 1.25-day year–1, but with inconclusive causes based on the parameters assessed in this study. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Dörr, J., Årthun, M., Eldevik, T., Madonna, E. 2021: Mechanisms of regional winter sea-ice variability in a warming Arctic. Journal of Climate. https://doi.org/10.1175/JCLI-D-21-0149.1 . Summary: The Arctic winter sea ice cover is in retreat overlaid by large internal variability. Changes to sea ice are driven by exchange of heat, momentum, and freshwater within and between the ocean and the atmosphere. Using a combination of observations and output from the Community Earth System Model Large Ensemble, we analyze and contrast present and future drivers of the regional winter sea ice cover. Consistent with observations and previous studies, we find that for the recent decades ocean heat transport though the Barents Sea and Bering Strait is a major source of sea ice variability in the Atlantic and Pacific sectors of the Arctic, respectively. Future projections show a gradually expanding footprint of Pacific and Atlantic inflows highlighting the importance of future Atlantification and Pacification of the Arctic Ocean. While the dominant hemispheric modes of winter atmospheric circulation are only weakly connected to the sea ice, we find distinct local atmospheric circulation patterns associated with present and future regional sea ice variability in the Atlantic and Pacific sectors, consistent with heat and moisture transport from lower latitudes. Even if the total freshwater input from rivers is projected to increase substantially, its influence on simulated sea ice is small in the context of internal variability. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Pecnjak, Martin (2021-08-05). Understanding the dynamics of recent Norwegian extreme weather events and their influence on energy production (Master’s thesis, University of Bergen, Bergen, Norway). https://bora.uib.no/bora-xmlui/handle/11250/2778409 . Summary: The growing frequency and severity of extreme weather events in the Northern Hemisphere has prompted a lot of research being done on their origin and physical mechanisms. Both simplified and complex approaches have been introduced in defining and understanding these events, where they look into high-amplitude quasi-stationary Rossby waves and their quasi-resonant amplification. However, different approaches exist to investigating extreme events and these were just a motivation for this thesis. Since the resonance method is suit- able mostly for summer events and the events discussed in this thesis have happened in all seasons, a different approach was needed. The events in question were a winter drought, two summer and autumn floods, a winter snowfall and a spring/summer heatwave in the areas of south and southwestern Norway. In order to detect certain features which would help solve this issue, we look into anomalies of different meteorological variables such as geopoten- tial height, surface temperature, precipitation and snowfall rate and zonal and meridional winds. Deep and thorough statistical and dynamical analyses are applied to define the out- comes and the physical origins which would help us obtain a clear picture on the whole case. The finite-amplitude local wave activity (LWA) diagnostic, as a measure of the meandering of the jet stream, has helped to give a clear picture along with the large-scale circulation. This method can be used as a proxy for the strength of the eddy-driven jet and the storm track. It has proven to be the key factor in defining what has exactly caused the events in ques- tion. The results and findings have shown that the LWA is a conclusive tool in determining whether an extreme event was related to a blocking pattern or not, while the LWA budget equation components have shed light on the so far poorly understood dynamical aspects which led to the events. The zonal LWA flux has proven to be a good predictor of blocking with its onset in the early stages of the events, similar to the traffic jam concept introduced by (Nakamura and Huang, 2018). The jet stream has a capacity for the LWA flux similar to how a highway has a capacity for the number of vehicles on it. If the capacity is exceeded, blocking occurs, and this is readily shown in the results and findings of this work. As for the budget equation components, the zonal LWA flux convergence has proven to be the key in maintaining the increase of the LWA as well as also having an early onset in each blocking event in agreement with the LWA flux. On the other hand, the residual in the LWA budget, which represents the non-conservative small-scale processes (diabatic sources and sinks of LWA), dampens the LWA. The LWA method has also proven to be useful in all seasons. The motivation for the thesis also came from the influence of the events on the meteorological variables related to the Norwegian energy production. The results show us clues into possible ways of improving forecasting of such events and minimizing their harmful impacts. They also show possibilities in improving energy management, infrastructure, allocation of resources and preparedness of the society for damages and hazards caused by the events. This was not fully investigated in this thesis and is the next step in the research of this topic. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Asbjørnsen, H., Johnson, H.L., Årthun, M. 2021: Variable Nordic Seas Inflow Linked to Shifts in North Atlantic Circulation. Journal of Climate. https://doi.org/10.1175/JCLI-D-20-0917.1 . Summary: 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. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Svendsen, L., Keenlyside, N., Muilwijk, M., Bethke, I., Omrani, N.-E., Gao, Y. 2021: Pacific contribution to decadal surface temperature trends in the Arctic during the twentieth century. Climate Dynamics. DOI: 10.1007/s00382-021-05868-9 . Summary: 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. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Koul, V., Sguotti, C., Årthun, M., Brune, S., Düsterhus, Bogstad, B., Ottersen, G., Baehr, J., Schrum, C. 2021: Skilful prediction of cod stocks in the North and Barents Sea a decade in advance. Nature Communications Earth & Environment. https://doi.org/10.1038/s43247-021-00207-6 . Summary: Reliable information about the future state of the ocean and fish stocks is necessary for informed decision-making by fisheries scientists, managers and the industry. However, regional ocean climate and fish stock predictions for the next few years, and up to 10 years, have until now had low forecast skill. In this article, the authors provide skilful forecasts of the biomass of cod stocks in the North and Barents Seas 10 years in advance. These point to a continuation of unfavorable oceanic conditions for the North Sea cod in the coming years, which would inhibit its recovery at present fishing levels, and a decrease in Northeast Arctic cod stock compared to the recent high levels. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Sandø, A.B., Mousing, E.A., Budgell, W.P., Hjøllo, S.S., Skogen, M.D., Ådlandsvik, B. 2021: Barents Sea plankton production and controlling factors in a fluctuating climate. Journal of Climate. https://doi.org/10.1175/JCLI-D-21-0149.1 . Summary: The Barents Sea and its marine ecosystem is exposed to many different processes related to the seasonal light variability, formation and melting of sea-ice, wind-induced mixing, and exchange of heat and nutrients with neighbouring ocean regions. A global model for the RCP4.5 scenario was downscaled, evaluated, and combined with a biophysical model to study how future variability and trends in temperature, sea-ice concentration, light, and wind-induced mixing potentially affect the lower trophic levels in the Barents Sea marine ecosystem. During the integration period (2010–2070), only a modest change in climate variables and biological production was found, compared to the inter-annual and decadal variability. The most prominent change was projected for the mid-2040s with a sudden decrease in biological production, largely controlled by covarying changes in heat inflow, wind, and sea-ice extent. The northernmost parts exhibited increased access to light during the productive season due to decreased sea-ice extent, leading to increased primary and secondary production in periods of low sea-ice concentrations. In the southern parts, variable access to nutrients as a function of wind-induced mixing and mixed layer depth were found to be the most dominating factors controlling variability in primary and secondary production. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Rieke, Ole (2021-06-01). Future Abrupt Changes in Winter Barents Sea Ice Area (Master’s thesis, University of Bergen, Bergen, Norway). https://bora.uib.no/bora-xmlui/handle/11250/2762637 . Summary: The Barents Sea is an area of strong anthropogenic winter sea ice loss that is superimposed by pronounced internal variability on interannual to multidecadal timescales. This internal variability represents a source of large uncertainty in future climate projections in the Barents Sea. This study aims to investigate internal variability of Barents Sea ice area and its driving mechanisms in future climate simulations of the Community Earth System Model Large Ensemble under the RCP8.5 climate scenario. We find that although sea ice area is projected to decline towards ice-free conditions, internal variability remains strong until late in the 21st century. A substantial part of this variability is expressed as events of abrupt change in the sea ice cover. These internally-driven events with a duration of 5-9 years can mask or enhance the anthropogenically-forced sea ice trend and lead to substantial ice growth or ice loss. Abrupt sea ice trends are a common feature of the Barents Sea in the future until the region becomes close to ice-free. Interannual variability in general, and in form of these sub-decadal events specifically, is forced by a combination of ocean heat transport, meridional winds and ice import, with ocean heat transport as the most dominant contributor. Our analysis shows that the influence of these mechanisms remains largely unchanged throughout the simulation. Investigation of a simulation from the same model where global warming is limited to 2°C shows that both mean and variability of sea ice area in the Barents Sea can be sustained at a substantial level in the future, and that abrupt changes can continue to occur frequently and produce sea ice cover of similar extent to present day climate. This highlights that future emissions play an essential role in the further decline of the Barents Sea winter sea ice cover. The results of this thesis contribute to a better understanding of Arctic sea ice variability on different time scales, and especially on the role of internal variability which is important in order to predict future sea ice changes under anthropogenic warming. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Rieke, Ole (2021-06-01). Future Abrupt Changes in Winter Barents Sea Ice Area (Master’s thesis, University of Bergen, Bergen, Norway). https://bora.uib.no/bora-xmlui/handle/11250/2762637 Summary: The Barents Sea is an area of strong anthropogenic winter sea ice loss that is superimposed by pronounced internal variability on interannual to multidecadal timescales. This internal variability represents a source of large uncertainty in future climate projections in the Barents Sea. This study aims to investigate internal variability of Barents Sea ice area and its driving mechanisms in future climate simulations of the Community Earth System Model Large Ensemble under the RCP8.5 climate scenario. We find that although sea ice area is projected to decline towards ice-free conditions, internal variability remains strong until late in the 21st century. A substantial part of this variability is expressed as events of abrupt change in the sea ice cover. These internally-driven events with a duration of 5-9 years can mask or enhance the anthropogenically-forced sea ice trend and lead to substantial ice growth or ice loss. Abrupt sea ice trends are a common feature of the Barents Sea in the future until the region becomes close to ice-free. Interannual variability in general, and in form of these sub-decadal events specifically, is forced by a combination of ocean heat transport, meridional winds and ice import, with ocean heat transport as the most dominant contributor. Our analysis shows that the influence of these mechanisms remains largely unchanged throughout the simulation. Investigation of a simulation from the same model where global warming is limited to 2°C shows that both mean and variability of sea ice area in the Barents Sea can be sustained at a substantial level in the future, and that abrupt changes can continue to occur frequently and produce sea ice cover of similar extent to present day climate. This highlights that future emissions play an essential role in the further decline of the Barents Sea winter sea ice cover. The results of this thesis contribute to a better understanding of Arctic sea ice variability on different time scales, and especially on the role of internal variability which is important in order to predict future sea ice changes under anthropogenic warming. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

EASAC – European Academies Science Advisory Council. 2021: A sea of change: Europe’s future in the Atlantic realm. EASAC policy report 42. ISBN: 978-3-8047-4262-8. https://easac.eu/publications/details/a-sea-of-change-europes-future-in-the-atlantic-realm/ PRESS RELEASE

Li, J., Li,, He, S., Wang, H., Orsolini, Y.J. 2021: The Atlantic Multidecadal Variability Phase Dependence of Teleconnection between the North Atlantic Oscillation in February and the Tibetan Plateau in March. J. Clim. https://doi.org/10.1175/JCLI-D-20-0157.1 . Summary: The Tibetan Plateau (TP), referred to as the “Asian water tower,” contains one of the largest land ice masses on Earth. The local glacier shrinkage and frozen-water storage are strongly affected by variations in surface air temperature over the TP (TPSAT), especially in springtime. This study reveals that the relationship between the February North Atlantic Oscillation (NAO) and March TPSAT is unstable with time and regulated by the phase of the Atlantic multidecadal variability (AMV). The significant out-of-phase connection occurs only during the warm phase of AMV (AMV+). The results show that during the AMV+, the negative phase of the NAO persists from February to March, and is accompanied by a quasi-stationary Rossby wave train trapped along a northward-shifted subtropical westerly jet stream across Eurasia, inducing an anomalous adiabatic descent that warms the TP. However, during the cold phase of the AMV, the negative NAO cannot persist into March. The Rossby wave train propagates along the well-separated polar and subtropical westerly jets, and the NAO–TPSAT connection is broken. Further investigation suggests that the enhanced synoptic eddy and low-frequency flow (SELF) interaction over the North Atlantic in February and March during the AMV+, caused by the southward-shifted storm track, helps maintain the NAO pattern via positive eddy feedback. This study provides a new detailed perspective on the decadal variability of the North Atlantic–TP connection in late winter to early spring. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Arthun, M., Wills, R. C. J., Johnson, H. L., Chafik, L., Langehaug, H. R. 2020: Mechanisms of decadal North Atlantic climate variability and implications for the recent cold anomaly. J Clim, 1-52. https://doi.org/10.1175/JCLI-D-20-0464.1 . Summary: Decadal sea surface temperature (SST) fluctuations in the North Atlantic Ocean influence climate over adjacent land areas and are a major source of skill in 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 preindustrial 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 suggests that the recent cold anomaly in the subpolar North Atlantic is, in part, a result of decadal SST variability. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Schevenhoven, Francine (2021-02-08). Training of supermodels in the context of weather and climate forecasting (PhD thesis, University of Bergen, Bergen, Norway). https://bora.uib.no/bora-xmlui/handle/11250/2727454 . Summary: Given a set of imperfect weather or climate models, predictions can be improved by combining the models dynamically into a so called `supermodel’. The models are optimally combined to compensate their individual errors. This is different from the standard multi-model ensemble approach (MME), where the model output is statistically combined after the simulations. Instead, the supermodel can create a trajectory closer to observations than any of the imperfect models. By intervening during the forecast, errors can be reduced at an early stage and the ensemble can exhibit different dynamical behavior than any of the individual models. In this way, common errors between the models can be removed and new, physically correct behavior can appear. In our simplified context of models sharing the same evolution function and phase space, we can define either a connected or a weighted supermodel. A connected supermodel uses nudging to bring the models closer together, while in a weighted supermodel all model states are replaced at regular time intervals (i.e., restarted) by the weighted average of the individual model states. To obtain optimal connection coefficients or weights, we need to train the supermodel on the basis of historical observations. A standard training approach such as minimization of a cost function requires many model simulations, which is computationally very expensive. This thesis has focused on developing two new methods to efficiently train supermodels. The first method is based on an idea called cross pollination in time, where models exchange states during the training. The second method is a synchronization-based learning rule, originally developed for parameter estimation. The techniques are developed on low-order systems, such as Lorenz63, and later applied to different versions of the intermediate-complexity global coupled atmosphere-ocean-land model SPEEDO. Here the observations are from the same models, but with different parameters. The applicability of the method to real observations is tested using sensitivity to noisy and incomplete data. The characteristics the individual models should have in order to be combined together into a supermodel are identified, as well as which physical variables should be connected in a supermodel, and which ones should not. Both training methods result in supermodels that outperform both the individual models and the MME, for short term predictions as well as long term simulations. Furthermore, we show that the novel use of negative weights can improve predictions in cases where model errors do not cancel (for instance, all models are too warm with respect to the truth). A crucial advantage of the proposed training schemes is that in the present context relatively short training periods suffice to find good solutions. Although the validity of our conclusions in the context of real observations and model scenarios has yet to be proved, our results are very encouraging. In principle, the methods are suitable to train supermodels constructed using state-of-the art weather and climate models. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Man, W., Zuo, M., Zhou, T., Fasullo, J. T., Bethke, I., Chen, X., Zou, L. Wu, B. 2021: Potential influences of volcanic eruptions on future global land monsoon precipitation changes. Earth’s Future. https://doi.org/10.1029/2020EF001803 . Summary: Understanding and predicting future global monsoon changes is critically important owing to its impacts on about two-thirds of population. Robust post-eruption signals in the monsoon climate raise the question of their potential for a role in future climate. However, major volcanic eruptions are generally not included in current projection scenarios because they are inherently unpredictable events. By using 60 plausible eruption scenarios sampled from reconstructed volcanic proxies over the past 2,500 years, we revealed the volcanic impacts on the future changes of summer precipitation over global and submonsoon regions. Episodic volcanic forcing not only leads to a 10% overall reduction of the centennial global land monsoon (GLM) precipitation, but also causes larger ensemble spread (∼20%) compared to no-volcanic and constant background-volcanic scenarios. Moreover, volcanic activity is projected to delay the time of emergence of anthropogenic GLM precipitation changes by five years on average over about 60% of the GLM area. Our results demonstrate the added value of incorporating major volcanic eruptions in monsoon projections. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Eldevik, T., Smedsrud, L.H., Li, C., Årthun, M., Madonna, E., Svendsen, L. 2020: The Arctic Mediterranean. In: Mechoso (Ed.). Interacting Climates of Ocean Basins Observations, Mechanisms, Predictability, and Impacts. Cambridge University Press, 2020, 186-215 . https://doi.org/10.1017/9781108610995.007 . Summary: The Arctic Mediterranean sits on the “top of the world” and connects the Atlantic and Pacific climate realms via the cold Arctic. It is the combined basin of the Nordic Seas (the Norwegian, Iceland, and Greenland seas) and the Arctic Ocean confined by the Arctic land masses – thus making it a Mediterranean ocean (Figure 6.1; e.g., Aagaard et al., 1985). The Arctic Mediterranean is small for a World Ocean but its heat loss and freshwater uptake is disproportionally large (e.g., Ganachaud and Wunsch, 2000; Eldevik and Nilsen, 2013; Haine et al., 2015). With the combined presence of the Gulf Stream’s northern limb, regional freshwater stratification, and a retreating sea-ice cover, it is likely where water mass contrasts, shifting air-ocean-ice interaction, and climate change are most pronounced in the present world oceans (Stocker et al., 2013; Vihma, 2014). You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Keenlyside, N., Y. Kosaka, N. Vigaud, A. Robertson, Y. Wang, D. Dommenget, J.-J. Luo, and D. Matei. 2020: Basin Interactions and Predictability, In: Mechoso (Ed.). Interacting Climates of Ocean Basins Observations, Mechanisms, Predictability, and Impacts. Cambridge University Press, 2020, 258-292 . Summary: The general public is familiar with weather forecasts and their utility, and the field of weather forecasting is well-established. Even the theoretical limit of the weather forecasting – two weeks – is known. In contrast, familiarity with climate prediction is low outside of the research field, the theoretical basis is not fully established, and we do not know the extent to which climate can be predicted. Variations in climate, however, can have large societal and economic consequences, as they can lead to droughts and floods, and spells of extreme hot and cold weather. Thus, improving our capabilities to predict climate is important and urgent, as it can enhance climate services and thereby contribute to the sustainable development of humans in this era of climate change. Link to chapter. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Counillon, F., Keenlyside, N., Toniazzo, T., Koseki, K., Demissie, T., Bethke, I., Wang, Y. 2021: Relating model bias and prediction skill in the equatorial Atlantic. Climate Dynamics. https://doi.org/10.1007/s00382-020-05605-8 For a nice overview of the article, check out this news piece by our partner NERSC, also involved in our collaborative projects TRIATLAS and STERCP. Summary: We investigate the impact of large climatological biases in the tropical Atlantic on reanalysis and seasonal prediction performance using the Norwegian Climate Prediction Model (NorCPM) in a standard and an anomaly coupled configuration. Anomaly coupling corrects the climatological surface wind and sea surface temperature (SST) fields exchanged between oceanic and atmospheric models, and thereby significantly reduces the climatological model biases of precipitation and SST. NorCPM combines the Norwegian Earth system model with the ensemble Kalman filter and assimilates SST and hydrographic profiles. We perform a reanalysis for the period 1980–2010 and a set of seasonal predictions for the period 1985–2010 with both model configurations. Anomaly coupling improves the accuracy and the reliability of the reanalysis in the tropical Atlantic, because the corrected model enables a dynamical reconstruction that satisfies better the observations and their uncertainty. Anomaly coupling also enhances seasonal prediction skill in the equatorial Atlantic to the level of the best models of the North American multi-model ensemble, while the standard model is among the worst. However, anomaly coupling slightly damps the amplitude of Atlantic Niño and Niña events. The skill enhancements achieved by anomaly coupling are largest for forecast started from August and February. There is strong spring predictability barrier, with little skill in predicting conditions in June. The anomaly coupled system show some skill in predicting the secondary Atlantic Niño-II SST variability that peaks in November–December from August 1st. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Asbjørnsen, Helene (2020-12-10). Mechanisms and pathways of ocean heat anomalies in the Arctic-Atlantic region (PhD thesis, University of Bergen, Bergen, Norway). https://bora.uib.no/bora-xmlui/handle/11250/2712025 . Summary: Along the Atlantic water pathway, from the Gulf Stream in the south to the Arctic Ocean in the north, variability in ocean heat content is pronounced on interannual to decadal time scales. Ocean heat anomalies in this Arctic-Atlantic sector are known to affect Arctic sea ice extent, marine ecosystems, and continental climate. However, there is at present neither consensus nor any complete understanding of the mechanisms causing such heat anomalies. This dissertation obtains a more robust understanding of regional ocean heat content variability by assessing the mechanisms and pathways of ocean heat anomalies in the Arctic-Atlantic region. The results are presented in three papers. The first paper investigates the link between a variable Nordic Seas inflow and large- scale ocean circulation changes upstream. Using a global, eddy-permitting ocean hind- cast together with a Lagrangian analysis tool, numerical particles are seeded at the Iceland-Scotland Ridge and tracked backward in time. Water from the subtropics sup- plied by the North Atlantic Current (NAC) is found to be the main component of the Nordic Seas inflow (64%), while 26% of the inflow has a subpolar or Arctic origin. Different atmospheric patterns are seen to affect the circulation strength along the advective pathways, as well as the supply of subtropical and Arctic-origin water to the ridge through shifts in the NAC and the subpolar front. A robust link between a high transport of Arctic-origin water and a cold and fresh inflow is furthermore established, while a high transport of subtropical water leads to higher inflow salinities. The second paper investigates the mechanisms of interannual heat content variability in the Norwegian Sea downstream of the Iceland-Scotland Ridge, using a state-of-the-art ocean state estimate and closed heat budget diagnostics. Ocean advection is found to be the primary contributor to heat content variability in the Atlantic domain of the Norwegian Sea, although local surface fluxes also play an active role. Anomalous heat advection furthermore depends on the strength of the Atlantic water inflow and the conditions upstream of the ridge. Combined, the two papers demonstrate the importance of gyre dynamics and large-scale wind forcing in causing variability at the ridge, while high- lighting the impacts on Norwegian Sea heat content downstream. For the third paper, warming trends in the Barents Sea and Fram Strait are explored, and, thus, the mechanisms underlying recent Atlantification of the Arctic Ocean. The Barents Sea is seen to transition to a warmer state, with reduced sea ice concentrations and Atlantic water extending further poleward. The mechanisms driving the warming are, however, found to be regionally dependent and not stationary in time. In the ice- free region, ocean advection is found to be a major driver of the warming trend due to increasing inflow temperatures in the late 1990s and early 2000s, while reduced ocean heat loss is contributing to the warming trend from the mid-2000s and onward. A considerable upper-ocean warming and a weakened stratification is seen in the ice- covered northwestern Barents Sea. However, in contrast to what has been previously hypothesized, the results do not point to increased upward heat fluxes from the Atlantic water layer to the Arctic surface layer as the source of the upper-ocean warming. The supply of Atlantic heat to the Nordic Seas and the Arctic Ocean has been scrutinized using both Lagrangian methods and heat budget diagnostics. Combined, the three papers demonstrate the important role of ocean heat transport in causing regional heat content variability and change in the Arctic-Atlantic region. A better understanding of interannual to decadal ocean heat content variability has implications for future prediction efforts, and for how we understand the ocean’s role in ongoing and future climate change. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Arthun, M., Onarheim, I. H., Dörr, J., Eldevik, T. 2020: The seasonal and regional transition to an ice‐free Arctic. Geophysical Research Letters 47. https://doi.org/10.1029/2020GL090825 Summary: We examine current and future Arctic sea ice loss in the latest generation of global climate models (CMIP6) focusing on regional and seasonal variability. We find that, unlike today, future Arctic sea ice loss will take place in all regions and all seasons. All Arctic shelf seas will become ice free in summer even if we follow a low emission scenario. Although future sea ice loss also takes place in winter, only the Barents Sea becomes ice free in winter before the end of this century. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Li, J., Li, F., Wang, H. 2020: Subseasonal prediction of winter precipitation in southern China using the early November snowpack over the Urals. Atmospheric and Oceanic Science Letters. https://doi.org/10.1080/16742834.2020.1824547 Summary: Evolution of the autumn snowpack has been considered as a potential source for the subseasonal predictability of winter surface air temperature, but its linkage to precipitation variability has been less well discussed. This study shows that the snow water equivalent (SWE) over the Urals region in early (1–14) November is positively associated with precipitation in southern China during 15–21 November and 6–15 January, based on the study period 1979/80–2016/17. In early November, a decreased Urals SWE warms the air locally via diabatic heating, indicative of significant land–atmosphere coupling over the Urals region. Meanwhile, a stationary Rossby wave train originates from the Urals and propagates along the polar-front jet stream. In mid (15–21) November, this Rossby wave train propagates downstream toward East Asia and, combined with the deepened East Asian trough, reduces the precipitation over southern China by lessening the water vapor transport. Thereafter, during 22 November to 5 January, there are barely any obvious circulation anomalies owing to the weak land–atmosphere coupling over the Urals. In early (6–15) January, the snowpack expands southward to the north of the Mediterranean Sea and cools the overlying atmosphere, suggestive of land–atmosphere coupling occurring over western Europe. A stationary Rossby wave train trapped in the subtropical westerly jet stream appears along with anomalous cyclonic circulation over Europe, and again with a deepened East Asian trough and less precipitation over southern China. The current findings have implications for winter precipitation prediction in southern China on the subseasonal timescale. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Smith, D.M., Scaife, A.A., Eade, R. et al. 2020: North Atlantic climate far more predictable than models imply. Nature. https://doi.org/10.1038/s41586-020-2525-0 . Summary: Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1,2,3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7,8,9 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 models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Brajard, J., Carrassi, A., Bocquet, M., Bertino, L. 2020: Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model. Geoscientific Model Development. https://doi.org/10.1016/j.jocs.2020.101171 . Summary: A novel method, based on the combination of data assimilation and machine learning is introduced. The new hybrid approach is designed for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting their future states. The method consists in applying iteratively a data assimilation step, here an ensemble Kalman filter, and a neural network. Data assimilation is used to optimally combine a surrogate model with sparse noisy data. The output analysis is spatially complete and is used as a training set by the neural network to update the surrogate model. The two steps are then repeated iteratively. Numerical experiments have been carried out using the chaotic 40-variables Lorenz 96 model, proving both convergence and statistical skill of the proposed hybrid approach. The surrogate model shows short-term forecast skill up to two Lyapunov times, the retrieval of positive Lyapunov exponents as well as the more energetic frequencies of the power density spectrum. The sensitivity of the method to critical setup parameters is also presented: the forecast skill decreases smoothly with increased observational noise but drops abruptly if less than half of the model domain is observed. The successful synergy between data assimilation and machine learning, proven here with a low-dimensional system, encourages further investigation of such hybrids with more sophisticated dynamics. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Vågane, Julie Solsvik (2020-06-26). Atlantic Multidecadal Variability (AMV) in the Norwegian Earth System model (Master’s thesis, University of Bergen, Bergen, Norway). http://bora.uib.no/handle/1956/22970 . Summary: The causes of low-frequency sea surface temperature (SST) variations in the Atlantic, known as Atlantic Multidecadal Variability (AMV), are debated. AMV has climatic impacts on for instance hurricane activity and Sahel rainfall, and understanding AMV can improve decadal predictions. While some discuss whether AMV arises due to external forcing, the ocean dynamics or the thermodynamic atmosphere-ocean interaction, others question the very existence of AMV. In this thesis, I look at the Norwegian Earth System Model (NorESM), investigating low-frequency variability and possible drivers for AMV in the North Atlantic. I compute a heat budget and a multiple linear regression (MLR) model, and investigate the influence of the dynamics and thermodynamics on AMV on different time scales and regions. I use the North Atlantic Oscillation (NAO) and the Atlantic Meridional Overturning circulation (AMOC) to characterize the large-scale impacts associated with ocean and atmospheric circulation patterns. The MLR model with NAO and AMOC, manages to explain 20.5 % of the temperature tendency on an interannual time scale, and 34.8 % on a decadal time scale in the subpolar gyre (SPG). In the tropics, the variance explained is smaller, only explaining 6.5 % interannually and 9.6 % decadally. Through a comparison with observations, I found that the AMOC amplitude is underestimated and the SST is off by over 1C. This may influence the performance of the MLR model. Finally, I present some ideas for improving the MLR model and the possibility for decadal predictions. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Asbjørnsen, H., Årthun, M., Skagseth, Ø., Eldevik, T. 2020: Mechanisms underlying recent Arctic Atlantification. Geophys. Res. Lett. https://doi.org/10.1029/2020GL088036 . Summary: Recent “Atlantification” of the Arctic is characterized by warmer ocean temperatures and a reduced sea ice cover. The Barents Sea is a “hot spot” for these changes, something which has broad socioeconomic and environmental impacts in the region. However, there is, at present, no complete understanding of what is causing the ocean warming. Here, we determine the relative importance of transport of heat by ocean currents (ocean advection) and heat exchanges between the atmosphere and the ocean (air-sea heat fluxes) in warming the Barents Sea and Fram Strait. In the ice-free region, ocean advection is found to be the main driver of the warming trend due to increasing inflow temperatures between 1996 and 2006. In the marginal ice zone and the ice-covered northern Barents Sea, ocean advection and air-sea heat fluxes are found to be of interchanging importance in driving the warming trend through the 1993–2014 period analyzed. A better understanding of the recent warming trends in the Barents Sea and Fram Strait has implications for how we understand the ocean’s role in ongoing and future Arctic climate change. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Fransner, F., Counillon, F., Bethke, I., Tjiputra, J., Samuelsen, A., Nummelin, A., Olsen, A. 2020: Ocean Biogeochemical Predictions—Initialization and Limits of Predictability. Front Mar Sci. https://doi.org/10.3389/fmars.2020.00386 . Summary: Predictions of ocean biogeochemistry, such as primary productivity and CO2 uptake, would help to understand the changing marine environment and the global climate. There is an emerging number of studies where initialization of ocean physics has led to successful predictions of ocean biogeochemistry. It is, however, unclear how much these predictions could be improved by also assimilating biogeochemical data to reduce uncertainties of the initial conditions. Further, the mechanisms that lead to biogeochemical predictability are poorly understood. Here we perform a suite of idealized twin experiments with an Earth System Model (ESM) with the aim to (i) investigate the role of biogeochemical tracers’ initial conditions on their predictability, and (ii) understand the physical processes that give rise to, or limit, predictability of ocean carbon uptake and export production. Our results suggest that initialization of the biogeochemical state does not significantly improve interannual-to-decadal predictions, which we relate to the strong control ocean physics exerts on the biogeochemical variability on these time scales. The predictability of ocean carbon uptake generally agrees well with the predictability of the mixed layer depth (MLD), suggesting that the predictable signal comes from the exchange of dissolved inorganic carbon (DIC) with deep-waters. The longest predictability is found in winter in at high latitudes, as for sea surface temperature and salinity, but the predictability of the MLD and carbon exchange is lower as it is more directly influenced by the atmospheric variability, e.g., the wind. The predictability of the annual mean export production is, on the contrary, nearly non-existing at high latitudes, despite the strong predictive skill for annual mean nutrient concentrations in these regions. This is related to the low predictability of the physical state of the summer surface ocean. Due to the shallow mixed layer it is decoupled from the ocean below and therefore strongly influenced by the chaotic atmosphere. Our results show that future studies need to target the predictability of the mixed layer to get a better understanding of the real-world predictability of ocean biogeochemistry. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Fransner F, Counillon F, Bethke I, Tjiputra J, Samuelsen A, Nummelin A, Olsen A. 2020: Ocean Biogeochemical Predictions – Initialization and Limits of Predictability. Front. Mar. Sci. DOI: https://doi.org/10.3389/fmars.2020.00386

Akinsanola, A. A., W. Zhou, T. Zhou, N. Keenlyside, 2020: Amplification of synoptic to annual variability of West African summer monsoon rainfall under global warming. npj Clim Atmos Sci. https://doi.org/10.1038/s41612-020-0125-1 . Summary: Increased knowledge of future changes in rainfall variability is needed to reduce vulnerability to potential impacts of global warming, especially in highly vulnerable regions like West Africa. While changes in mean and extreme rainfall have been studied extensively, rainfall variability has received less attention, despite its importance. In this study, future changes in West African summer monsoon (WASM) rainfall variability were investigated using data from two regional climate models that participated in the Coordinated Regional Climate Downscaling Experiment (CORDEX). The daily rainfall data were band-pass filtered to isolate variability at a wide range of timescales. Under global warming, WASM rainfall variability is projected to increase by about 10–28% over the entire region and is remarkably robust over a wide range of timescales. We found that changes in mean rainfall significantly explain the majority of intermodel spread in projected WASM rainfall variability. The role of increased atmospheric moisture is examined by estimating the change due to an idealized local thermodynamic enhancement. Analysis reveals that increased atmospheric moisture with respect to warming following the Clausius–Clapeyron relationship can explain the majority of the projected changes in rainfall variability at all timescales. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Skagseth, Ø., Eldevik, T., Årthun, M., Asbjørnsen, H., Lien, V.S., Smedsrud, L.H. 2020: Reduced efficiency of the Barents Sea cooling machine. Nature Climate Change. https://doi.org/10.1038/s41558-020-0772-6 Summary: Dense water masses from the Barents Sea are an important part of the Arctic thermohaline system. Here, using hydrographic observations from 1971 to 2018, we show that the Barents Sea climate system has reached a point where ‘the Barents Sea cooling machine’—warmer Atlantic inflow, less sea ice, more regional ocean heat loss—has changed towards less-efficient cooling. Present change is dominated by reduced ocean heat loss over the southern Barents Sea as a result of anomalous southerly winds. The outflows have accordingly become warmer. Outflow densities have nevertheless remained relatively unperturbed as increasing salinity appears to have compensated the warming inflow. However, as the upstream Atlantic Water is now observed to freshen while still relatively warm, we speculate that the Barents Sea within a few years may export water masses of record-low density to the adjacent basins and deep ocean circulation. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Skagseth, Ø., Eldevik, T., Årthun, M., Asbjørnsen, H., Lien, VS., Smedsrud, LH. 2020: Decreasing efficiency of the Barents Sea cooling machine. Nature Climate Change. https://doi.org/10.1038/s41558-020-0772-6 . Summary: Dense water masses from the Barents Sea are an important part of the Arctic thermohaline system. Here, using hydrographic observations from 1971 to 2018, we show that the Barents Sea climate system has reached a point where ‘the Barents Sea cooling machine’—warmer Atlantic inflow, less sea ice, more regional ocean heat loss—has changed towards less-efficient cooling. Present change is dominated by reduced ocean heat loss over the southern Barents Sea as a result of anomalous southerly winds. The outflows have accordingly become warmer. Outflow densities have nevertheless remained relatively unperturbed as increasing salinity appears to have compensated the warming inflow. However, as the upstream Atlantic Water is now observed to freshen while still relatively warm, we speculate that the Barents Sea within a few years may export water masses of record-low density to the adjacent basins and deep ocean circulation. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

King MP, Yu, E, Sillmann J. 2020: Impact of strong and extreme El Niños on European hydroclimate. Tellus A. https://doi.org/10.1080/16000870.2019.1704342

Michel, C., C. Li, I.R. Simpson, I. Bethke, M.P. King, and S. Sobolowski. 2020: The change in the ENSO teleconnection under a low global warming scenario and the uncertainty due to internal variability. J Clim. https://doi.org/10.1175/JCLI-D-19-0730.1

Sorto et al. 2019: Ocean Reanalyses: Recent Advances and Unsolved Challenges. Front Mar Sci https://doi.org/10.3389/fmars.2019.00418

Wohland, J., N. E. Omrani, N. Keenlyside, and D. Witthaut. 2019: Significant multidecadal variability in German wind energy generation, Wind Energy Science, 4(3), 515-526 https://doi.org/10.5194/wes-4-515-2019

Schevenhoven, F., F. Selten, A. Carrassi, and N. Keenlyside, 2019: Improving weather and climate predictions by training of supermodels. Earth System Dynamics. https://doi.org/10.5194/esd-2019-32

F. Li, Y. J. Orsolini, N. Keenlyside, M.‐L. Shen, F. Counillon, Y. G. Wang, 2019. Impact of Snow Initialization in Subseasonal‐to‐Seasonal Winter Forecasts with the Norwegian Climate Prediction Model. JGR Atmospheres https://doi.org/10.1029/2019JD030903

Svendsen, L., N. Keenlyside, I. Bethke, Y. Gao, and N.-E. Omrani, 2018: Pacific contribution to the early twentieth-century warming in the Arctic. Nature Climate Change, 8, 793-797. DOI: https://doi.org/10.1038/s41558-018-0247-1

Omrani, N.-E., F. Ogawa, H. Nakamura, N. Keenlyside, S. W. Lubis, and K. Matthes, 2019: Key Role of the Ocean Western Boundary currents in shaping the Northern Hemisphere climate. Scientific Reports, 9, 3014. DOI: https://doi.org/10.1038/s41598-019-39392-y

Ogawa, F., N. Keenlyside, Y. Gao, T. Koenigk, S. Yang, L. Suo, T. Wang, G. Gastineau, T. Nakamura, N. Cheung Ho, N. E. Omrani, J. Ukita, and V. Semenov, 2018: Evaluating Impacts of Recent Arctic Sea Ice Loss on the Northern Hemisphere Winter Climate Change. Geophysical Research Letters, 45, 3255-3263. DOI: https://doi.org/10.1002/2017GL076502

Langehaug, H. R., A. B. Sandø, M. Årthun, M. Ilıcak, 2019: Variability along the Atlantic water pathway in the forced Norwegian Earth System Model. Climate Dynamics, 52, 1211-1230. DOI: https://doi.org/10.1007/s00382-018-4184-5

Langehaug, H. R., D. Matei, T. Eldevik, K. Lohmann, and Y. Gao, 2017: On model differences and skill in predicting sea surface temperature in the Nordic and Barents Seas. Climate Dynamics, 48, 913-933. DOI: https://doi.org/10.1007/s00382-016-3118-3

Kimmritz, M., F. Counillon, C. M. Bitz, F. Massonnet, I. Bethke, and Y. Gao, 2017: Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model. Tellus A,, 70. DOI: https://doi.org/10.1080/16000870.2018.1435945

Counillon, F., N. Keenlyside, I. Bethke, Y. Wang, S. Billeau, M. L. Shen, and M. Bentsen, 2016: Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model. Tellus A, 68, DOI: https://doi.org/10.3402/tellusa.v68.32437

Årthun, M., T. Eldevik, L. H. Smedsrud, 2019. The role of Atlantic heat transport in future Arctic winter sea ice loss. Journal of Climate, 32(11), 3327-3341. DOI: https://doi.org/10.1175/JCLI-D-18-0750.1

Årthun, M., E. W. Kolstad, T. Eldevik, and N. S. Keenlyside, 2018: Time Scales and Sources of European Temperature Variability. Geophysical Research Letters, 45, 3597-3604. DOI: https://doi.org/10.1002/2018GL077401

Årthun, M., B. Bogstad, U. Daewel, N. S. Keenlyside, A. B. Sandø, C. Schrum, and G. Ottersen, 2018: Climate based multi-year predictions of the Barents Sea cod stock. PLOS ONE, 13, e0206319. DOI: https://doi.org/10.1371/journal.pone.0206319

Årthun, M., T. Eldevik, E. Viste, H. Drange, T. Furevik, H. L. Johnson, and N. S. Keenlyside, 2017: Skillful prediction of northern climate provided by the ocean. Nature Communications, 8, 15875. DOI: https://doi.org/10.1038/ncomms15875

Luo H, Zheng F, Keenlyside N, Zhu J. 2020: Ocean–atmosphere coupled Pacific Decadal variability simulated by a climate model. Clim Dyn. https://doi.org/10.1007/s00382-020-05248-9 . Summary: Currently, the mechanisms for Pacific Decadal Oscillation (PDO) are still disputed, and in particular the atmosphere response to the ocean in the mid-latitude remains a key uncertainty. In this study, we investigate two potential feedbacks—a local positive and a delayed negative—for the PDO based on a long-term control simulation using the ECHAM5/MPI-OM coupled model, which is selected because of reproduces well the variability of PDO. The positive feedback is as follows. In the PDO positive phase, the meridional sea surface temperature (SST) gradient is intensified and this strengthens the lower level atmospheric baroclinicity in the mid-latitudes, leading to the enhancement of Aleutian low and zonal wind. These atmospheric changes reinforce the meridional SST temperature gradient through the divergence of ocean surface currents. The increased heat flux loss over the anomalously warm water and decreased heat flux loss over the anomalously cold water in turn reinforce the lower atmospheric meridional temperature gradient, baroclinicity and atmospheric circulation anomalies, forming a local positive feedback for the PDO. The delayed negative feedback arises, because the intensified meridional SST gradient also generates an anticyclonic wind stress in the central North Pacific, warming the upper ocean by Ekman convergence. The warm upper ocean anomalies then propagate westward and are transported to the mid-latitudes in the western North Pacific by the western boundary current. This finally reduces the meridional SST gradient, 18 years after the peak PDO phase. These results demonstrate the significant contributions of the meridional SST gradient to the PDO’s evolution. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Dai, P., Gao, Y., Counillon, F., Wang, Y., Kimmritz, M., Langehaug, H.R. 2020: Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model. Clim Dyn 54, 3863–3878. https://doi.org/10.1007/s00382-020-05196-4 . Summary: The version of the Norwegian Climate Prediction Model (NorCPM) that only assimilates sea surface temperature (SST) with the Ensemble Kalman Filter has been used to investigate the seasonal to decadal prediction skill of regional Arctic sea ice extent (SIE). Based on a suite of NorCPM retrospective forecasts, we show that seasonal prediction of pan-Arctic SIE is skillful at lead times up to 12 months, which outperforms the anomaly persistence forecast. The SIE skill varies seasonally and regionally. Among the five Arctic marginal seas, the Barents Sea has the highest SIE prediction skill, which is up to 10–11 lead months for winter target months. In the Barents Sea, the skill during summer is largely controlled by the variability of solar heat flux and the skill during winter is mostly constrained by the upper ocean heat content/SST and also related to the heat transport through the Barents Sea Opening. Compared with several state-of-the-art dynamical prediction systems, NorCPM has comparable regional SIE skill in winter due to the improved upper ocean heat content. The relatively low skill of summer SIE in NorCPM suggests that SST anomalies are not sufficient to constrain summer SIE variability and further assimilation of sea ice thickness or atmospheric data is expected to increase the skill. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Shen H., Li F., He S., Orsolini Y.J., Li J. 2020: Impact of late spring Siberian snow on summer rainfall in South-Central China. Clim. Dyn. 54: 3803–3818. DOI: https://doi.org/10.1007/s00382-020-05206-5 . Summary: Located in the Yangtze River Valley and surrounded by mountains, South-Central China (SCC) frequently suffered from natural disasters such as torrential precipitation, landslide and debris flow. Here we provide corroborative evidence for a link between the late spring (May) snow water equivalent (SWE) over Siberia and the summer (July–August, abbr. JA) rainfall in SCC. We show that, in May, anomalously low SWE over Siberia is robustly related to a large warming from the surface to the mid-troposphere, and to a stationary Rossby wave train from Siberia eastward toward the North Atlantic. On the one hand, over the North Atlantic there exhibits a tripole pattern response of sea surface temperature anomalies in May. It persists to some extent in JA and in turn triggers a wave train propagating downstream across Eurasia and along the Asian jet, as the so-called Silk Road pattern (SRP). On the other hand, over northern Siberia the drier soil occurs in JA, accompanied by an overlying anomalous anticyclone through the positive feedback. This anomalous anticyclone favors the tropospheric cooling over southern Siberia, and the meridional (northward) displacement of the Asian jet (JMD) due to the change in the meridional temperature gradient. The combination of the SRP and the JMD facilitates less water vapor transport from the tropical oceans and anomalous descending motion over SCC, and thus suppresses the precipitation. These findings indicate that May Siberian SWE can be exploited for seasonal predictability of SCC precipitation. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Tondeur, M., Carrassi, A., Vannitsem, S., Bocquet, M. 2020: On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter. J Stat Phys 179, 1161–1185. https://doi.org/10.1007/s10955-020-02525-z . Summary: Data assimilation for systems possessing many scales of motions is a substantial methodological and technological challenge. Systems with these features are found in many areas of computational physics and are becoming common thanks to increased computational power allowing to resolve finer scales and to couple together several sub-components. Coupled data assimilation (CDA) distinctively appears as a main concern in numerical weather and climate prediction with major efforts put forward by meteo services worldwide. The core issue is the scale separation acting as a barrier that hampers the propagation of the information across model components (e.g. ocean and atmosphere). We provide a brief survey of CDA, and then focus on CDA using the ensemble Kalman filter (EnKF), a widely used Monte Carlo Gaussian method. Our goal is to elucidate the mechanisms behind information propagation across model components. We consider first a coupled system of equations with temporal scale difference, and deduce that: (i) cross components effects are strong from the slow to the fast scale, but, (ii) intra-component effects are much stronger in the fast scale. While observing the slow scale is desirable and benefits the fast, the latter must be observed with high frequency otherwise the error will grow up to affect the slow scale. Numerical experiments are performed using the atmosphere-ocean model, MAOOAM. Six configurations are considered, differing for the strength of the atmosphere-ocean coupling and/or the number of model modes. The performance of the EnKF depends on the model configuration, i.e. on its dynamical features. A comprehensive dynamical characterisation of the model configurations is provided by examining the Lyapunov spectrum, Kolmogorov entropy and Kaplan–Yorke attractor dimension. We also compute the covariant Lyapunov vectors and use them to explain how model instabilities act on different model’s modes according to the coupling strength. The experiments confirm the importance of observing the fast scale, but show also that, despite its slow temporal scale, frequent observations in the ocean are beneficial. The relation between the ensemble size, N, and the unstable subspace dimension, n0, has been studied. Results largely ratify what known for uncoupled system: the condition N≥n0 is necessary for the EnKF to work satisfactorily. Nevertheless the quasi-degeneracy of the Lyapunov spectrum of MAOOAM, with many near-zero exponents, is potentially the cause of the smooth gradual reduction of the analysis error observed for some model configurations, even when N>n0. Future prospects for the EnKF in the context of coupled ocean-atmosphere systems are finally discussed. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Sandø, A.B., Johansen, G.O., Aglen, A., Stiansen, J.E., Renner, A.H.H. 2020: Climate Change and New Potential Spawning Sites for Northeast Arctic cod. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00028 Summary: In this study we investigate both historical and potential future changes in the spatial distribution of spawning habitats for Northeast Arctic cod (NEA cod) based on a literature study on spawning habitats and different physical factors from a downscaled climate model. The approach to use a high resolution regional ocean model to analyze spawning sites is new and provides more details about crucial physical factors than a global low resolution model can. The model is evaluated with respect to temperature and salinity along the Norwegian coast during the last decades and shows acceptable agreement with observations. However, the model does not take into consideration biological or evolutionary factors which also have impact on choice of spawning sites. Our results from the downscaled RCP4.5 scenario suggest that the spawning sites will be shifted further northeastwards, with new locations at the Russian coast close to Murmansk over the next 50 years, where low temperatures for many decades in the last century were a limiting factor on spawning during spring. The regional model gives future temperatures above the chosen lower critical minimum value in larger areas than today and indicates that spawning will be more extensive there. Dependent on the chosen upper temperature boundary, future temperatures may become a limiting factor for spawning habitats at traditional spawning sites south of Lofoten. Finally, the observed long-term latitudinal shifts in spawning habitats along the Norwegian coast the recent decades may be indirectly linked to temperature through the latitudinal shift of the sea ice edge and the corresponding shift in available ice-free predation habitats, which control the average migration distance to the spawning sites. We therefore acknowledge that physical limitations for defining the spawning sites might be proxies for other biophysically related factors. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Shah, A., Bertino, L., Counillon, C., El Gharamti, M., Xie, J. 2019: Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment. Tellus A: Dynamic Meteorology and Oceanography. https://doi.org/10.1080/16000870.2019.1697166 Summary: A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thickness measurements, as obtained from passive microwave remote sensing, with respect to more trivial solutions like neglecting the out-of-range values or assimilating climatology instead. Some known properties inherent to the EnKF-SQ are evaluated: the tendency to draw the solution closer to the thickness threshold, the skewness of the resulting analysis ensemble and the potential appearance of outliers. The experiments show that none of these properties prove deleterious in light of the other sub-optimal characters of the sea ice data assimilation system used here (non-linearities, non-Gaussian variables, lack of strong coupling). The EnKF-SQ has a single tuning parameter that is adjusted for best performance of the system at hand. The sensitivity tests reveal that the tuning parameter does not critically influence the results. The EnKF-SQ makes overall a valid approach for assimilating semi-qualitative observations into high-dimensional nonlinear systems. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Kimmritz, M., F. Counillon, L. H. Smedsrud, I. Bethke, N. Keenlyside, F. Ogawa, and Y. Wang:. 2019: Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic. JAMES https://doi.org/10.1029/2019MS001825 . Summary:The declining Arctic sea ice entails both risks and opportunities for the Arctic ecosystem, communities, and economic activities. Reliable seasonal predictions of the Arctic sea ice could help to guide decisionmakers to benefit from arising opportunities and to mitigate increased risks in the Arctic. However, despite some success, seasonal prediction systems in the Arctic have not exploited their full potential yet. For instance, so far only a single model component, for example, the ocean, has been updated in isolation to derive a skillful initial state, though joint updates across model components, for example, the ocean and the sea ice, are expected to perform better. Here, we introduce a system that, for the first time, deploys joint updates of the ocean and the sea ice state, using data of the ocean hydrography and sea ice concentration, for seasonal prediction in the Arctic. By comparing this setup with a system that updates only the ocean in isolation, we assess the added skill of facilitating sea ice concentration data to jointly update the ocean and the sea ice. While the update of the ocean alone leads to skillful winter predictions only in the North Atlantic, the joint update strongly enhances the overall skill. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Jackson, L.C., Dubois C., Forget G., Haines, K., Harrison, M., Iovino, D., Köhl, A., Mignac, D., Masina, S., Peterson, K.A., Piecuch, C.G., Roberts, C.D., Robson, J., Storto, A., Toyoda, T., Valivieso, M., Wilson, C., Wang, Y., Zuo, H. 2019: The Mean State and Variability of the North Atlantic Circulation: A Perspective From Ocean Reanalyses. JGR Oceans. https://doi.org/10.1029/2019JC015210 . Summary: The observational network around the North Atlantic has improved significantly over the last few decades revealing changes over decadal time scales in the North Atlantic, including in heat content, heat transport, and the circulation. However, there are still significant gaps in the observational coverage. Ocean reanalyses fill in these gaps by combining the observations with a computer model of the ocean to give consistent estimates of the ocean state. These reanalyses are potentially useful tools that can be used to understand the observed changes; however, their skill must also be assessed. We use an ensemble of global ocean reanalyses in order to examine the mean state and variability of the North Atlantic ocean since 1993. In particular, we examine the convection, circulation, transports of heat and fresh water, and temperature and salinity changes. We find that reanalyses show some consistency in their results, suggesting that they may be useful for understanding circulation changes in regions and times where there are no observations. We also show improvements in some aspects of the ocean circulation as the observational coverage has improved. This highlights the importance of continuing observational campaigns. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Wang, Y., F. Counillon, N. Keenlyside, L. Svendsen, S. Gleixner, M. Kimmritz, P. Dai, and Y. Gao, 2019: Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF. Climate Dynamics. https://doi.org/10.1007/s00382-019-04897-9 . Summary:This study demonstrates that assimilating SST with an advanced data assimilation method yields prediction skill level with the best state-of-the-art systems. We employ the Norwegian Climate Prediction Model (NorCPM)—a fully-coupled forecasting system—to assimilate SST observations with the ensemble Kalman filter. Predictions of NorCPM are compared to predictions from the North American Multimodel Ensemble (NMME) project. The global prediction skill of NorCPM at 6- and 12-month lead times is higher than the averaged skill of the NMME. A new metric is introduced for ranking model skill. According to the metric, NorCPM is one of the most skilful systems among the NMME in predicting SST in most regions. Confronting the skill to a large historical ensemble without assimilation, shows that the skill is largely derived from the initialisation rather than from the external forcing. NorCPM achieves good skill in predicting El Niño–Southern Oscillation (ENSO) up to 12 months ahead and achieves skill over land via teleconnections. However, NorCPM has a more pronounced reduction in skill in May than the NMME systems. An analysis of ENSO dynamics indicates that the skill reduction is mainly caused by model deficiencies in representing the thermocline feedback in February and March. We also show that NorCPM has skill in predicting sea ice extent at the Arctic entrance adjacent to the north Atlantic; this skill is highly related to the initialisation of upper ocean heat content. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Penny SG et al. 2019: Observational needs for improving ocean and coupled reanalysis, S2S Prediction, and decadal prediction. Front Mar Sci. https://doi.org/10.3389/fmars.2019.00391 . Summary: Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational long-range prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods. From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of ocean-observing data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network. Link to review article. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Asbjørnsen, H., M. Årthun, Ø. Skagseth, Eldevik, T. 2019: Mechanisms of ocean heat anomalies in the Norwegian Sea. JGR Oceans. https://doi.org/10.1029/2018JC014649 Summary: Ocean heat content in the Norwegian Sea exhibits pronounced variability on interannual to decadal time scales. These ocean heat anomalies are known to influence Arctic sea ice extent, marine ecosystems, and continental climate. It nevertheless remains unknown to what extent such heat anomalies are produced locally within the Norwegian Sea, and to what extent the region is more of a passive receiver of anomalies formed elsewhere. A main practical challenge has been the lack of closed heat budget diagnostics. In order to address this issue, a regional heat budget is calculated for the Norwegian Sea using the ECCOv4 ocean state estimate—a dynamically and kinematically consistent model framework fitted to ocean observations for the period 1992–2015. The depth-integrated Norwegian Sea heat budget shows that both ocean advection and air-sea heat fluxes play an active role in the formation of interannual heat content anomalies. A spatial analysis of the individual heat budget terms shows that ocean advection is the primary contributor to heat content variability in the Atlantic domain of the Norwegian Sea. Anomalous heat advection furthermore depends on the strength of the Atlantic water inflow, which is related to large-scale circulation changes in the subpolar North Atlantic. This result suggests a potential for predicting Norwegian Sea heat content based on upstream conditions. However, local surface forcing (air-sea heat fluxes and Ekman forcing) within the Norwegian Sea substantially modifies the phase and amplitude of ocean heat anomalies along their poleward pathway, and, hence, acts to limit predictability. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.