Scientific breakthrough: Winter climate in Norway now more predictable

Scientists from the Bjerknes Climate Prediction Unit, affiliated with the Nansen Environmental and Remote Sensing Center, the Bjerknes Centre for Climate Research, and the University of Bergen, contributed to a recent publication in Nature. The results indicate that it is possible to predict how the atmospheric circulation above the North Atlantic will evolve during the next decade. This is crucial for better predicting the winters in Europe and Eastern North America.

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.
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.”

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 (Master’s thesis)

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.

Climate Futures: New Centre for Research-based Innovation

Press release from NORCE (in Norwegian)

The Norwegian Research Council has given Climate Futures the prestigious status as a Centre for Research-based Innovation (SFI).

Climate Futures is a new and ambitious action to generate long-term cooperation between companies, public organizations and research groups across sectors and disciplines to tackle one of the most urgent challenges of our time.

The changing nature of weather and climate poses a severe threat to the prosperity and well-being of our economy and society as a whole, but climate risk is inadequately managed due to knowledge gaps and deficiencies in the decision-making processes of businesses and public authorities.

– These are fantastic news. We knew that the theme of Climate Futures was relevant, and we are pleased that the Research Council also sees that climate risk is an area that requires great effort on the research front. We at NORCE and the Bjerknes Centre have a brilliant group of research partners, business world stakeholders and public sector partners. We are now looking forward to helping these deal with the great risk associated with weather and climate, whether for direct phenomena such as floods and droughts, or more transferred risk related to investments in other parts of the world, says centre manager and climate scientist Erik Kolstad in NORCE and the Bjerknes Centre.

Climate Futures is led by NORCE, and is comprised of seven other research partners and close to 30 stakeholder partners, representing agriculture, renewable energy, disaster mitigation, shipping, insurance, finance, risk management, and the public sector.

They will work together to create new solutions to predict and manage climate risk from 10 days to 10 years into the future.

Contact
Erik Kolstad, centre leader Climate Futures, NORCE and the Bjerknes Centre. +47 411 22 457
Trond Martin Dokken, Executive Vice President climate, NORCE   +47 975 64 402

Research partners in Climate Futures
NHH / SNF, Universitetet i Bergen, Norsk regnesentral, Meteorologisk institutt og Nansensenteret.
NORCE, UiB og Nansensenteret er alle samarbeidspartnere i Bjerknessenteret for klimaforskning.

Stakeholder partners
BKK, Golden Ocean, Gartnerhallen, Graminor, MOWI, StormGeo, Agder Energi, Tryg Forsikring, Norges Bondelag, Western Bulk, KLP, G2 Ocean, Safetec, Statkraft, Norsk Landbrukrådgiving, Vestland Fylkeskommune, Viken Fylkeskommune, Rogaland Fylkeskommune, Alle fylkesmennene i Norge, representert ved Fylkesmannen i Vestland og Direktoratet for Samfunnssikkerhet og Beredskap (DSB).

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, V. S., Smedsrud, LH. 2020: Reduced efficiency of the Barents Sea cooling machine. Nature Climate Change. https://doi.org/10.1038/s41558-020-0772-6 .

Summary: Dense water masses from the Barents Sea are an important part of the Arctic thermohaline system. Here, using hydrographic observations from 1971 to 2018, we show that the Barents Sea climate system has reached a point where ‘the Barents Sea cooling machine’—warmer Atlantic inflow, less sea ice, more regional ocean heat loss—has changed towards less-efficient cooling. Present change is dominated by reduced ocean heat loss over the southern Barents Sea as a result of anomalous southerly winds. The outflows have accordingly become warmer. Outflow densities have nevertheless remained relatively unperturbed as increasing salinity appears to have compensated the warming inflow. However, as the upstream Atlantic Water is now observed to freshen while still relatively warm, we speculate that the Barents Sea within a few years may export water masses of record-low density to the adjacent basins and deep ocean circulation.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.