Tag: keenlyside

New EASAC report: “A Sea of Change”

Translated from the Norwegian press release at the Bjerknes Centre for Climate Research

Tor Eldevik leads EASAC report, “A sea of change: Europe’s future in the Atlantic realm”.

In the report an international panel of experts goes through the changes seen until now in the Atlantic Ocean, and what we can expect of climate change. But there is also a potential in being the closest neighbour to our western ocean.

The report is published by EASAC, the European science academy advisory council. The panel of experts is led by Tor Eldevik, Professor at the University of Bergen and the Bjerknes Centre for Climate Research, and Deputy Leader in the BCPU.

A potential in climate prediction

The report shows how fluctuations and trends in the Atlantic Ocean affects the climate in Europe and both the environment and resources in the ocean and on land.

“The report is very clear about future climatic risks, but equally focuses on the future benefits we can harvest from better understanding of the relations between the state of the Atlantic and climatic conditions over Europe that affects everything from the supply of renewable energy to fisheries,” says Tor Eldevik.

He emphasises how this knowledge can be used far better than it is now. Climate predictions developed today have the potential to predict cod movements between years, including movements out of Norwegian fisheries sectors.

To power companies the knowledge of how westerlies in the Atlantic Ocean (NAO index) affect Norwegian hydro power production can also be useful.

Figurtekst: Norsk vasskraftproduksjon svinger saman med vestavindsbeltet i Atlanterhavet, slik tidlegare vist av Helene Asbjørnsen og Noel Keenlyside UiB og Bjerknessenteret. Vasskraftdata frå SSB, styrke på vestavind vinterstid (NAO-indeks) frå climatedataguide.ucar.edu
Figure 4.1 Norwegian hydropower production swings with the westerly winds (wintertime NAO; variance explained 40%). (Source: H. Asbjørnsen and N. Keenlyside, University of Bergen / Bjerknes Climate Prediction Unit; power production and NAO data from https://www.ssb.no/en/statbank/table/08307 and https://climatedataguide.ucar.edu/climate-data/hurrell-north-atlanticoscillation-nao-index-station-based, respectively.)

Climate risk

Tor Eldevik points out how future changes in the ocean are connected to how successful we are at mitigating global warming.

“If we succeed in keeping the average warming to 1.5°C, then Antarctica may continue melting at current rates; but overshooting the 2 °C Paris Agreement target towards 3°C may lead to Antarctic melt alone add 0.5 cm a year by 2100,” he says.

Sea level rise have regional differences, but to the many million people living by the North Sea Basin, accounting for a meter rise in sea level.

Cities along the coast of the Netherlands, Germany, Denmark and Great Britain will be affected greatly.

Figure 2.5 The North Sea coastline with +1 m of global SLR with the flooded areas in blue. Major population centres are marked in circles. (Source: https://sealevel.climatecentral.org/maps/.)
Figure 2.5 The North Sea coastline with +1 m of global SLR with the flooded areas in blue. Major population centres are marked in circles. (Source: https://sealevel.climatecentral.org/maps/.)

Central points in the report

  • Sea level rise
    On average, the sea level has risen 11-16 centimeters in the twentieth century.
    Europe must prepare for up to one meter sea level rise by 2100. Storm surges on a level we now expect every 100 years, could be yearly by 2100 if CO2 emissions continues as today. Ice melts on Greenland and the Antarctic contributes to sea level rise, as well as glacial metling in warmer areas and sea water expanding with heat. There is uncertainty linked to melting on Greenland and the Antarctic which needs to be followed closely.
  • Renewable energy
    Wind, weather and precipitation over Europe, and especially the Norwegian coast, kan be linked to the ocean. The strength of the Gulf Stream and the westerlies over the Atlantic Ocean affects the severity of wind and precipication over Europe, including the Norwegian coast. This knowledge is critical to predict climate fluctuations for the coming years and seasons – which in Norway is especially useful to power companies, both wind and hydro energy production.
  • Ocean acidification
    Temperature increases leads to fish stocks moving, uptake of CO2 makes the ocean more acidic, which changes the living conditions for life in the ocean. If the current emissions of climate gases is kept up, we will reach a level in 2100 that is uninhabitable.
  • Ocean circulation, ocean streams and the Gulf Stream giving us a milder climate
    Speculations that the Gulf Stream will stop are excessive. But the Gulf Stream strength are connected to climate in Europe and Norway. A decline in heat transportation of 20% is expected further South in the Atlantic this century, but as far North as Norway we are likely to see an increase in the stream and a continued heating of the ocean.

Read the report with EASAC

 

 

The Future Atlantic Ocean: Forecasting ecosystem functioning from microbiomes to fisheries

Side event at the All Atlantic Conference 2021, where climate forecasting on a broad level was discussed. BCPU has contributing members in the EU Horizon 2020 projects TRIATLAS and Blue Action, who were organising the event with projects AtlantECO and Mission Atlantic.

Watch the presentations and following discussion on Youtube:

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.

Link to publication. 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, 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.

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.

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.”

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.

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.