• Spatial patterns, mechanisms and predictability of Barents Sea ice change

    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.

  • NorCPM1 and its contribution to CMIP6 DCPP

    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” 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.

  • Twenty-one years of phytoplankton bloom phenology in the Barents, Norwegian and North seas

    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 . 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.

  • Mechanisms of regional winter sea-ice variability in a warming Arctic

    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.

  • Understanding the dynamics of recent Norwegian extreme weather events and their influence on energy production

    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.

  • Variable Nordic Seas Inflow Linked to Shifts in North Atlantic Circulation

    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.

  • Pacific contribution to decadal surface temperature trends in the Arctic during the twentieth century

    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.

  • Skilful prediction of cod stocks in the North and Barents Sea a decade in advance

    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.

  • Barents Sea plankton production and controlling factors in a fluctuating climate

    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.

  • Future Abrupt Changes in Winter Barents Sea Ice Area

    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.

  • The Atlantic Multidecadal Variability phase dependence of teleconnection between the North Atlantic Oscillation in February and the Tibetan Plateau in March

    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.

  • Mechanisms of decadal North Atlantic climate variability and implications for the recent cold anomaly

    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.

  • Training of supermodels in the context of weather and climate forecasting

    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.

  • Potential influences of volcanic eruptions on future global land monsoon precipitation changes

    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.

  • The Arctic Mediterranean In Interacting Climates of Ocean Basins Observations, Mechanisms, Predictability, and Impacts

    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.

  • Basin Interactions and Predictability. In: Interacting Climates of Ocean Basins: Observations, Mechanisms, Predictability, and Impacts

    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.

  • Relating model bias and prediction skill in the equatorial Atlantic

    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 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.

  • Mechanisms and pathways of ocean heat anomalies in the Arctic-Atlantic region

    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.

  • The seasonal and regional transition to an ice‐free Arctic

    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.

  • North Atlantic climate far more predictable than models imply

    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.

  • Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model.

    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.

  • Atlantic Multidecadal Variability (AMV) in the Norwegian Earth System model

    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.

  • Mechanisms underlying recent Arctic Atlantification

    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.

  • Ocean Biogeochemical Predictions—Initialization and Limits of Predictability

    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.

  • Amplification of synoptic to annual variability of West African summer monsoon rainfall under global warming

    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.

  • Reduced efficiency of the Barents Sea cooling machine

    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.

  • Ocean–atmosphere coupled Pacific Decadal variability simulated by a climate model

    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.

  • Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model

    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.

  • Impact of late spring Siberian snow on summer rainfall in South-Central China

    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.

  • On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter

    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.

  • Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment.

    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.

  • Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic

    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.

  • The Mean State and Variability of the North At-lantic Circulation: A Perspective From Ocean Reanalyses

    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.

  • Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF

    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.

  • Observational needs for improving ocean and coupled reanalysis, S2S Prediction, and decadal prediction

    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.

  • Mechanisms of ocean heat anomalies in the Norwegian Sea

    Asbjørnsen, H., M. Årthun, Ø. Skagseth, T. Eldevik, 2019.. Journal of Geophysical Research, 124(4). DOI: https://doi.org/10.1029/2018JC014649