Tag: bethke

Impact of initialization methods on the predictive skill in NorCPM: an Arctic–Atlantic case study

Passos, L., Langehaug, HR., Årthun, M., Eldevik, T., Bethke, I., Kimmritz, M. 2022: Impact of initialization methods on the predictive skill in NorCPM: an Arctic–Atlantic case study. Clim Dyn. https://doi.org/10.1007/s00382-022-06437-4

Summary: The skilful prediction of climatic conditions on a forecast horizon of months to decades into the future remains a main scientific challenge of large societal benefit. Here we assess the hindcast skill of the Norwegian Climate Prediction Model (NorCPM) for sea surface temperature (SST) and sea surface salinity (SSS) in the Arctic–Atlantic region focusing on the impact of different initialization methods. We find the skill to be distinctly larger for the Subpolar North Atlantic than for the Norwegian Sea, and generally for all lead years analyzed. For the Subpolar North Atlantic, there is furthermore consistent benefit in increasing the amount of data assimilated, and also in updating the sea ice based on SST with strongly coupled data assimilation. The predictive skill is furthermore significant for at least two model versions up to 8–10 lead years with the exception for SSS at the longer lead years. For the Norwegian Sea, significant predictive skill is more rare; there is relatively higher skill with respect to SSS than for SST. A systematic benefit from more complex data assimilation approach can not be identified for this region. Somewhat surprisingly, skill deteriorates quite consistently for both the Subpolar North Atlantic and the Norwegian Sea when going from CMIP5 to corresponding CMIP6 versions. We find this to relate to change in the regional performance of the underlying physical model that dominates the benefit from initialization.

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WMO Global Annual to Decadal Climate Update: A Prediction for 2021–25

Hermanson, L., Smith, D., Seabrook, M., Bilbao, R., Doblas-Reyes, F., Tourigny, E., Lapin, V., Kharin, V.V., Merryfield, W.J., Sospedra-Alfonso, R., Athanasiadis, P., Nicoli, D., Gualdi, S., Dunstone, N., Eade, R., Scaife, A., Collier, M., O’Kane, T., Kitsios, V., Sandery, P., Pankatz, K., Früh, B., Pohlmann, H., Müller, W., Kataoka, T., Tatebe, H., Ishii M., Imada, Y., Kruschke, T., Koenigk, T., Pasha Karami, M., Yang, S., Tian, T., Zhang, L., Delworth, T., Yang, X., Zeng, F., Wang, Y., Counillon, F., Keenlyside, N.S., Bethke, I., Lean, J., Luterbacher, J., Kumar Kolli, R., Kumar, A. 2022: WMO Global Annual to Decadal Climate Update: A Prediction for 2021–25. BAMS https://doi.org/10.1175/BAMS-D-20-0311.1 .

Summary: As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future..

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Propagation of Thermohaline Anomalies and Their Predictive Potential along the Atlantic Water Pathway

Langehaug, H. R., Ortega, P., Counillon, F., Matei, D., Maroon, E., Keenlyside, N., Mignot, J., Wang, Y., Swingedouw, D., Bethke, I., Yang, S., Danabasoglu, G., Bellucci, A., Ruggieri, P., Nicolì, D., Årthun, M. 2022: Propagation of Thermohaline Anomalies and Their Predictive Potential along the Atlantic Water Pathway. J Clim. https://doi.org/10.1007/s10236-022-01523-x

Summary: In this study, we find that dynamical prediction systems and their respective climate models struggle to realistically represent ocean surface temperature variability in the eastern subpolar North Atlantic and Nordic seas on interannual-to-decadal time scales. In previous studies, ocean advection is proposed as a key mechanism in propagating temperature anomalies along the Atlantic water pathway toward the Arctic Ocean. Our analysis suggests that the predicted temperature anomalies are not properly circulated to the north; this is a result of model errors that seems to be exacerbated by the effect of initialization shocks and forecast drift. Better climate predictions in the study region will thus require improving the initialization step, as well as enhancing process representation in the climate models.

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NorCPM1 and its contribution to CMIP6 DCPP

Bethke, I., Wang, Y., Counillon, F., Keenlyside, N., Kimmritz, M., Fransner, F., Samuelsen, A., Langehaug, H., Svendsen, L., Chiu, P.-G., Passos, L., Bentsen, M., Guo, C., Gupta, A., Tjiputra, J., Kirkevåg, A., Olivié, D., Seland, Ø., Solsvik Vågane, J., Fan, Y., Eldevik, T. 2021: NorCPM1 and its contribution to CMIP6 DCPP. Geosci Model Dev. https://doi.org/10.5194/gmd-14-7073-2021 .

For an easy-to-understand overview, we recommend starting with this neat article written by the Climate Futures team, a project connected to BCPU: “New Study: Decadal Climate Forecasts From The Norwegian Climate Prediction Model” (les heller på norsk).

Summary: The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It combines the Norwegian Earth System Model version 1 (NorESM1) – which features interactive aerosol–cloud schemes and an isopycnic-coordinate ocean component with biogeochemistry – with anomaly assimilation of sea surface temperature (SST) and -profile observations using the ensemble Kalman filter (EnKF).

We describe the Earth system component and the data assimilation (DA) scheme, highlighting implementation of new forcings, bug fixes, retuning and DA innovations. Notably, NorCPM1 uses two anomaly assimilation variants to assess the impact of sea ice initialization and climatological reference period: the first (i1) uses a 1980–2010 reference climatology for computing anomalies and the DA only updates the physical ocean state; the second (i2) uses a 1950–2010 reference climatology and additionally updates the sea ice state via strongly coupled DA of ocean observations.

We assess the baseline, reanalysis and prediction performance with output contributed to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). The NorESM1 simulations exhibit a moderate historical global surface temperature evolution and tropical climate variability characteristics that compare favourably with observations. The climate biases of NorESM1 using CMIP6 external forcings are comparable to, or slightly larger than those of, the original NorESM1 CMIP5 model, with positive biases in Atlantic meridional overturning circulation (AMOC) strength and Arctic sea ice thickness, too-cold subtropical oceans and northern continents, and a too-warm North Atlantic and Southern Ocean. The biases in the assimilation experiments are mostly unchanged, except for a reduced sea ice thickness bias in i2 caused by the assimilation update of sea ice, generally confirming that the anomaly assimilation synchronizes variability without changing the climatology. The i1 and i2 reanalysis/hindcast products overall show comparable performance. The benefits of DA-assisted initialization are seen globally in the first year of the prediction over a range of variables, also in the atmosphere and over land. External forcings are the primary source of multiyear skills, while added benefit from initialization is demonstrated for the subpolar North Atlantic (SPNA) and its extension to the Arctic, and also for temperature over land if the forced signal is removed. Both products show limited success in constraining and predicting unforced surface ocean biogeochemistry variability. However, observational uncertainties and short temporal coverage make biogeochemistry evaluation uncertain, and potential predictability is found to be high. For physical climate prediction, i2 performs marginally better than i1 for a range of variables, especially in the SPNA and in the vicinity of sea ice, with notably improved sea level variability of the Southern Ocean. Despite similar skills, i1 and i2 feature very different drift behaviours, mainly due to their use of different climatologies in DA; i2 exhibits an anomalously strong AMOC that leads to forecast drift with unrealistic warming in the SPNA, whereas i1 exhibits a weaker AMOC that leads to unrealistic cooling. In polar regions, the reduction in climatological ice thickness in i2 causes additional forecast drift as the ice grows back. Posteriori lead-dependent drift correction removes most hindcast differences; applications should therefore benefit from combining the two products.

The results confirm that the large-scale ocean circulation exerts strong control on North Atlantic temperature variability, implying predictive potential from better synchronization of circulation variability. Future development will therefore focus on improving the representation of mean state and variability of AMOC and its initialization, in addition to upgrades of the atmospheric component. Other efforts will be directed to refining the anomaly assimilation scheme – to better separate internal and forced signals, to include land and atmosphere initialization and new observational types – and improving biogeochemistry prediction capability. Combined with other systems, NorCPM1 may already contribute to skilful multiyear climate prediction that benefits society.

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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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Relating model bias and prediction skill in the equatorial Atlantic

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

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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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.

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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

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.

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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.

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