Category: Publications

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.

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Future Abrupt Changes in Winter Barents Sea Ice Area (Master’s thesis)

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.

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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.

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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. 2021: 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.

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Training of supermodels in the context of weather and climate forecasting (PhD thesis)

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.

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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.

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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).

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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.

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Relating model bias and prediction skill in the equatorial Atlantic

Counillon, F., Keenlyside, N., Toniazzo, T., Koseki, S., 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.

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