Keenlyside, N., Y. Kosaka, N. Vigaud, A. Robertson, Y. Wang, D. Dommenget, J.-J. Luo, and D. Matei. 2020: Basin Interactions and Predictability, In: Mechoso (Ed.). Interacting Climates of Ocean Basins Observations, Mechanisms, Predictability, and Impacts. Cambridge University Press, 2020, 258-292 .
Summary: The general public is familiar with weather forecasts and their utility, and the field of weather forecasting is well-established. Even the theoretical limit of the weather forecasting – two weeks – is known. In contrast, familiarity with climate prediction is low outside of the research field, the theoretical basis is not fully established, and we do not know the extent to which climate can be predicted. Variations in climate, however, can have large societal and economic consequences, as they can lead to droughts and floods, and spells of extreme hot and cold weather. Thus, improving our capabilities to predict climate is important and urgent, as it can enhance climate services and thereby contribute to the sustainable development of humans in this era of climate change.
Link to chapter. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.
Counillon, F., Keenlyside, N., Toniazzo, T., Koseki, 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
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.
Hver dag i desember la Bjerknessenteret ut en video med en av senterets klimaforskere.
Helene Langehaug er forsker ved Bjerknessenteret og Nansensenteret for miljø- og fjernmåling, og jobber blant annet i Bjerknes Climate Prediction Unit. Hun er en av dem på Bjerknessenteret som lenge har jobbet med klimavarsling. For å kunne gjøre det, er havet ekstra viktig. Det er havet som er klimaets hukommelse, og som legger mye av grunnlaget for å se lengre frem enn det værvarslingen for øyeblikket kan gjøre.
Asbjørnsen, Helene (2020-12-10). Mechanisms and pathways of ocean heat anomalies in the Arctic-Atlantic region (PhD thesis, University of Bergen, Bergen, Norway). https://bora.uib.no/bora-xmlui/handle/11250/2712025 .
Summary: Along the Atlantic water pathway, from the Gulf Stream in the south to the Arctic Ocean in the north, variability in ocean heat content is pronounced on interannual to decadal time scales. Ocean heat anomalies in this Arctic-Atlantic sector are known to affect Arctic sea ice extent, marine ecosystems, and continental climate. However, there is at present neither consensus nor any complete understanding of the mechanisms causing such heat anomalies. This dissertation obtains a more robust understanding of regional ocean heat content variability by assessing the mechanisms and pathways of ocean heat anomalies in the Arctic-Atlantic region. The results are presented in three papers.
The first paper investigates the link between a variable Nordic Seas inflow and large- scale ocean circulation changes upstream. Using a global, eddy-permitting ocean hind- cast together with a Lagrangian analysis tool, numerical particles are seeded at the Iceland-Scotland Ridge and tracked backward in time. Water from the subtropics sup- plied by the North Atlantic Current (NAC) is found to be the main component of the Nordic Seas inflow (64%), while 26% of the inflow has a subpolar or Arctic origin. Different atmospheric patterns are seen to affect the circulation strength along the advective pathways, as well as the supply of subtropical and Arctic-origin water to the ridge through shifts in the NAC and the subpolar front. A robust link between a high transport of Arctic-origin water and a cold and fresh inflow is furthermore established, while a high transport of subtropical water leads to higher inflow salinities. The second paper investigates the mechanisms of interannual heat content variability in the Norwegian Sea downstream of the Iceland-Scotland Ridge, using a state-of-the-art ocean state estimate and closed heat budget diagnostics. Ocean advection is found to be the primary contributor to heat content variability in the Atlantic domain of the Norwegian Sea, although local surface fluxes also play an active role. Anomalous heat advection furthermore depends on the strength of the Atlantic water inflow and the conditions upstream of the ridge. Combined, the two papers demonstrate the importance of gyre dynamics and large-scale wind forcing in causing variability at the ridge, while high- lighting the impacts on Norwegian Sea heat content downstream.
For the third paper, warming trends in the Barents Sea and Fram Strait are explored, and, thus, the mechanisms underlying recent Atlantification of the Arctic Ocean. The Barents Sea is seen to transition to a warmer state, with reduced sea ice concentrations and Atlantic water extending further poleward. The mechanisms driving the warming are, however, found to be regionally dependent and not stationary in time. In the ice- free region, ocean advection is found to be a major driver of the warming trend due to increasing inflow temperatures in the late 1990s and early 2000s, while reduced ocean heat loss is contributing to the warming trend from the mid-2000s and onward. A considerable upper-ocean warming and a weakened stratification is seen in the ice- covered northwestern Barents Sea. However, in contrast to what has been previously hypothesized, the results do not point to increased upward heat fluxes from the Atlantic water layer to the Arctic surface layer as the source of the upper-ocean warming.
The supply of Atlantic heat to the Nordic Seas and the Arctic Ocean has been scrutinized using both Lagrangian methods and heat budget diagnostics. Combined, the three papers demonstrate the important role of ocean heat transport in causing regional heat content variability and change in the Arctic-Atlantic region. A better understanding of interannual to decadal ocean heat content variability has implications for future prediction efforts, and for how we understand the ocean’s role in ongoing and future climate change.
Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.
Arthun, M., Onarheim, I. H., Dörr, J., Eldevik, T. 2020: The seasonal and regional transition to an ice‐free Arctic. Geophysical Research Letters 47. https://doi.org/10.1029/2020GL090825
Summary: We examine current and future Arctic sea ice loss in the latest generation of global climate models (CMIP6) focusing on regional and seasonal variability. We find that, unlike today, future Arctic sea ice loss will take place in all regions and all seasons. All Arctic shelf seas will become ice free in summer even if we follow a low emission scenario. Although future sea ice loss also takes place in winter, only the Barents Sea becomes ice free in winter before the end of this century.
Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.
Li, J., Li, F., Wang, H. 2020: Subseasonal prediction of winter precipitation in southern China using the early November snowpack over the Urals. Atmospheric and Oceanic Science Letters. https://doi.org/10.1080/16742834.2020.1824547
Summary: Evolution of the autumn snowpack has been considered as a potential source for the subseasonal predictability of winter surface air temperature, but its linkage to precipitation variability has been less well discussed. This study shows that the snow water equivalent (SWE) over the Urals region in early (1–14) November is positively associated with precipitation in southern China during 15–21 November and 6–15 January, based on the study period 1979/80–2016/17. In early November, a decreased Urals SWE warms the air locally via diabatic heating, indicative of significant land–atmosphere coupling over the Urals region. Meanwhile, a stationary Rossby wave train originates from the Urals and propagates along the polar-front jet stream. In mid (15–21) November, this Rossby wave train propagates downstream toward East Asia and, combined with the deepened East Asian trough, reduces the precipitation over southern China by lessening the water vapor transport. Thereafter, during 22 November to 5 January, there are barely any obvious circulation anomalies owing to the weak land–atmosphere coupling over the Urals. In early (6–15) January, the snowpack expands southward to the north of the Mediterranean Sea and cools the overlying atmosphere, suggestive of land–atmosphere coupling occurring over western Europe. A stationary Rossby wave train trapped in the subtropical westerly jet stream appears along with anomalous cyclonic circulation over Europe, and again with a deepened East Asian trough and less precipitation over southern China. The current findings have implications for winter precipitation prediction in southern China on the subseasonal timescale.
Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.
Værfenomenet El Niño forbinder mange med intens varme, men nå er dens kaldere lillesøster La Niña her. Hva betyr det? Vår forsker og førsteamanuensis, Dr. Lea Svendsen, skriver om dette hos Enegi og Klima: Hva gjør La Niña med været?.
(Most are all quite familiar with the El Niño phenomenon and its link with periods of intense heat. But now, El Niño’s colder little sister is here. What does this mean for the weather? Our researcher, Dr. Lea Svendsen, writes about this in her recent article in the Norwegian popular science journal Energi og Klima (link above. Article in Norwegian).
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.
Figure 1: Rainfall variation over Northern Europe between 1960 and 2005. e) shows observations (black) and modelled predictions (red) with uncertainty range (shaded red) without adjustments, f) shows the improved and adjusted modelled predictions and uncertainty range.
Investigating the climate of the past
In order to look forward in time, looking at the past is helpful. This is true in many cases, and the researchers behind this study led by the UK Met Office made use of this principle. They used climate models for investigating how accurately climate can be predicted on a decadal scale over the past sixty years.
Sea level pressure above the North Atlantic influences Norwegian winters
The main pattern of changes in sea level pressure above the North Atlantic, called the North Atlantic Oscillation (NAO), influences the wind and storms over the North Atlantic, which in turn influences the winter weather in Europe and Eastern North America. Two extremes are possible for winters in these regions: stormy, warm, and wet, or calm, cold, and dry. Which extreme the winter weather will tend towards is now shown to be very predictable on a decadal scale, according to the new study.
The researchers investigated the North Atlantic Oscillation and its influence by producing retrospective forecasts of the past climate (called hindcasts) and comparing them to observations made in the past. That way they quantified how accurate the model predictions are.
One of the most important predictions for Europe and especially Norway is the amount of rainfall. The comparison between hindcasts produced by models (Figure f, red line) and the observation (Figure f, black line) shows that the rainfall over Northern Europe can be predicted with high certainty. The model results match the previous observations nicely.
Contribution from the Bjerknes Climate Prediction Unit
Many hindcasts were produced by different research groups worldwide. The different climate models from these groups are part of t experiments performed for the last and upcoming Intergovernmental Panel on Climate Change (IPCC) reports. Bergen researchers involved in the study are the following: Noel Keenlyside (UiB/NERSC), François Counillon (NERSC), Ingo Bethke (UiB), and Yiguo Wang (NERSC). The four are part of the Bjerknes Climate Prediction Unit at the Bjerknes Centre for Climate Research. They used their climate model, the Norwegian Climate Prediction Model (NorCPM), which is part of CMIP6, to contribute to this study.
Climate models need to be improved
Apart from the high predictability of the North Atlantic climate indicated by the hindcasts, the study also shows that current climate models are underestimating this exact fact (Figure e). The researchers identified this deficiency and show that climate models need to be and can be adjusted (Figure f) to better predict the behaviour of the pressure above the North Atlantic and in turn the future winter conditions in Europe and Eastern North America.
To sum it up, confidently predicting the winters of the next years for Norway is now a reality, but climate models need to be improved.
Significance of this study: Climate can now be better predicted on short time scales
Noel Keenlyside, leader of the BCPU, commented “This is a major breakthrough for climate research and for the development of climate services in our region. Now we have solid evidence that we can provide to our stakeholders, like BKK and Agder Energi, that we can really say something useful about how the coming winters will be. It will also lead to improved models for providing better long-term projections of climate change.
The newly established Centre for Research-Based Innovation (SFI) called Climate Futures led by NORCE, with the Bjerknes Centre and Nansen Center as partners, among others, will benefit from this work in the future. The Centre’s objective is to improve climate prediction on short time scales of days to decades, and to improve the management of climate risks. By improving the predictability of Norwegian winters on a decadal scale, as indicated by this recent study, decadal climate prediction will become better and better. Erik Kolstad with NORCE and Bjerknes Centre leads this project:
“These results show that the models now can predict the climate in a useful way for planning in a number of sectors, like renewable energy, agriculture, and finance/insurance. With predictions like these both the business world and the public sector will be better prepared for extreme weather events and potentially gain more from periods of favorable weather and climate.”
Tarjei Breiteig (Head of Hydroglogy and Meterology at Agder Energi AS) represents one of the stakeholders this study directly impacts.
“This study shows that there is stilled untapped potential in saying something about possible weather and climate the next decade. To save hydropower in years of little demand, and have stored hydropower in years where demand will be high, it is essential for us to have sufficient information on what fluctuations to be expected in weather and climate the next decade. The climate research groups in Bergen show that they take this effort seriously, and that they are ahead when it comes to analyse and use climate models in the real world.”
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.
