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.
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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.
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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.”
F. Li, Y. J. Orsolini, N. Keenlyside, M.‐L. Shen, F. Counillon, Y. G. Wang, 2019. Impact of Snow Initialization in Subseasonal‐to‐Seasonal Winter Forecasts with the Norwegian Climate Prediction Model. JGR Atmospheres
Counillon, F., N. Keenlyside, I. Bethke, Y. Wang, S. Billeau, M. L. Shen, and M. Bentsen, 2016: Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model. Tellus A, 68,
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.
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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.
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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.
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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.
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