Category: Publications2023

Publications that published in 2023

Identifying quasi-periodic variability using multivariate empirical mode decomposition: a case of the tropical Pacific

Boljka, L., Omrani, N.-E., Keenlyside, N. S. 2023: Identifying quasi-periodic variability using multivariate empirical mode decomposition: a case of the tropical Pacific. Weather Clim Dynam. https://doi.org/10.5194/wcd-4-1087-2023

Summary: A variety of statistical tools have been used in climate science to gain a better understanding of the climate system’s variability on various temporal and spatial scales. However, these tools are mostly linear, stationary, or both. In this study, we use a recently developed nonlinear and nonstationary multivariate time series analysis tool – multivariate empirical mode decomposition (MEMD). MEMD is a powerful tool for objectively identifying (intrinsic) timescales of variability within a given spatio-temporal system without any timescale pre-selection. Additionally, a red noise significance test is developed to robustly extract quasi-periodic modes of variability. We apply these tools to reanalysis and observational data of the tropical Pacific. This reveals a quasi-periodic variability in the tropical Pacific on timescales ∼ 1.5–4.5 years, which is consistent with El Niño–Southern Oscillation (ENSO) – one of the most prominent quasi-periodic modes of variability in the Earth’s climate system. The approach successfully confirms the well-known out-of-phase relationship of the tropical Pacific mean thermocline depth with sea surface temperature in the eastern tropical Pacific (recharge–discharge process). Furthermore, we find a co-variability between zonal wind stress in the western tropical Pacific and the tropical Pacific mean thermocline depth, which only occurs on the quasi-periodic timescale. MEMD coupled with a red noise test can therefore successfully extract (nonstationary) quasi-periodic variability from the spatio-temporal data and could be used in the future for identifying potential (new) relationships between different variables in the climate system.

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Initialization shock in the ocean circulation reduces skill in decadal predictions of the North Atlantic subpolar gyre

Polkova, I, Swingedouw, D., Hermanson, L., Köhl, A., Stammer, D., Smith, D., Kröger, J., Bethke, I., Yang, X., Zhang, L., Nicolì, D., Athanasiadis, P., Karami, P., Pankatz, K., Pohlmann, H., Wu, B., Bilbao, R., Ortega, P., Yang, S., Sospedra-Alfonso, R., Merryfield, W., Kataoka, T., Tatebe, H., Imada, Y., Ishii, M., Matear, R. 2023: Initialization shock in the ocean circulation reduces skill in decadal predictions of the North Atlantic subpolar gyre. Front Clim. doi: https://doi.org/10.3389/fclim.2023.1273770

Summary: Due to large northward heat transport, the Atlantic meridional overturning circulation (AMOC) strongly affects the climate of various regions. Its internal variability has been shown to be predictable decades ahead within climate models, providing the hope that synchronizing ocean circulation with observations can improve decadal predictions, notably of the North Atlantic subpolar gyre (SPG). Climate predictions require a starting point which is a reconstruction of the past climate. This is usually performed with data assimilation methods that blend available observations and climate model states together. There is no unique method to derive the initial conditions. Moreover, this can be performed using full-field observations or their anomalies superimposed on the model’s climatology to avoid strong drifts in predictions. How critical ocean circulation drifts are for prediction skill has not been assessed yet. We analyze this possible connection using the dataset of 12 decadal prediction systems from the World Meteorological Organization Lead Centre for Annual-to-Decadal Climate Prediction. We find a variety of initial AMOC errors within the predictions related to a dynamically imbalanced ocean states leading to strongly displaced or multiple maxima in the overturning structures. This likely results in a blend of what is known as model drift and initial shock. We identify that the AMOC initialization influences the quality of the SPG predictions. When predictions show a large initial error in their AMOC, they usually have low skill for predicting internal variability of the SPG for a time horizon of 6-10 years. Full-field initialized predictions with low AMOC drift show better SPG skill than those with a large AMOC drift. Nevertheless, while the anomaly-initialized predictions do not experience large drifts, they show low SPG skill when skill also present in historical runs is removed using a residual correlation metric. Thus, reducing initial shock and model biases for the ocean circulation in prediction systems might help to improve their prediction for the SPG beyond 5 years. Climate predictions could also benefit from quality-check procedure for assimilation/initialization because currently the research groups only reveal the problems in initialization once the set of predictions has been completed, which is an expensive effort.

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Prediction of Harmful Algae Blooms Impacting Shellfish Farms in Norway (PhD thesis)

Silva, Edson (2023-11-30). Prediction of Harmful Algae Blooms Impacting Shellfish Farms in Norway. PhD thesis, University of Bergen, Norway. https://bora.uib.no/bora-xmlui/handle/11250/3104786

Summary: Harmful algae blooms (HABs) cause severe damage to the ecosystem and human health, and have significant economic impacts on shellfish farms. HAB prediction models have become increasingly popular because they can help stakeholder to take mitigation actions and reduce economic loss. Few studies have attempted to predict toxic algae species related to shellfish contamination because the time extent of data is limited and modeling the environmental response of specific taxa is complex. However, toxic algae monitoring programs have now been running for several years and have produced large datasets of toxic algae. Combined with long-time series observations by satellites and model reanalysis, we can now calibrate prediction models for toxic algae affecting shellfish farms. This thesis calibrates machine learning models to predict toxic algae impacting shellfish farms in Norwegian coastal waters for the first time. It is conducted by combining toxic algae data from the Norwegian Food Safety Authority with satellite observations of Chla concentration, Suspended Particulate Matter (SPM), Sea Surface Temperature (SST), Photosynthetically Active Radiation (PAR), and wind speed, as well as model reanalysis data of Mixed Layer Depth (MLD) and Sea Surface Salinity (SSS). Paper I demonstrates that the blooms phenology has a strong interannual variability in the North, Norwegian, and Barents Seas, which is related to the variability of the environmental ocean and atmospheric factors (SST, MLD, SPM, and winds). It implies that these variables are potential predictors for blooms in the region. Paper II exhibit that a Support Vector Machine (SVM) model can predict the presence probability of eight toxic algae on the Norwegian coast using SST, PAR, SSS, and MLD. The models can also predict the probability of harmful levels for Alexandrium spp., Alexandrium tamarense, Dinophysis acuta, and Azadinium spinosum. It can produce a climatological overview of the HABs along the Norwegian coast and provide monitoring and prediction applications. Paper III extends the SVM application to the prediction of D. acuminata abundance in a sub-seasonal range (7 -28 days) when fed with the current and past D. acuminata abundance, SST, PAR, and wind speed. The sub-seasonal forecast model is developed for the Lyngenfjord in northern Norway as a proof of concept. The probability estimates in Paper II and the sub-seasonal forecast of D. acuminata abundance in Paper III are two complementary approaches. The first is employable in the entire coast even where algae monitoring is unavailable, while the latter requires tuning to specific aquaculture farms and can achieve refined prediction. Since the SVM models are fed with data commonly available worldwide, they are portable to other regions where data from harmful algae monitoring programs are available.

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Arctic-Atlantic Climate Variability and Predictability in Observations and in a Dynamical Prediction System (PhD thesis)

Goncalves Dos Passos, Leilane (2023-11-03): Arctic-Atlantic Climate Variability and Predictability in Observations and in a Dynamical Prediction System. PhD thesis, University of Bergen, Bergen, Norway. https://bora.uib.no/bora-xmlui/handle/11250/3099594

Summary: The major focus of this thesis is on understanding decadal climate predictability to improve climate models and their predictions. Climate predictions show promising results but are still facing challenges, especially in connecting the ocean and atmosphere. The ocean is the main source of predictability. The ocean’s capacity to store and release heat over long periods of time makes it a thermal memory of the climate system. In the Arctic-Atlantic region, ocean currents transport heat to polar areas, and along this path, the ocean releases the heat to the atmosphere through surface fluxes. From this interaction, both the ocean and the atmosphere change. On the one hand, as the ocean releases heat into the atmosphere, it cools down, increasing its density. The denser water eventually flows southward as part of the Atlantic Meridional Overturning Circulation (AMOC). On the other hand, the atmosphere being warmed by the ocean affects nearby land areas through the winds, influencing the climate variability of Western Europe.
This dynamic ocean-atmosphere interaction is a source of predictability in the Arctic-Atlantic region and is investigated here using observations and a dynamical prediction system, the Norwegian Climate Prediction Model (NorCPM). Dynamical prediction systems are useful tools for investigating and predicting climate variability on decadal timescales. Beginning their development in the early 2000s, these systems are currently the focus of significant efforts by the scientific community to provide operational decadal forecasts with reliable and accurate information. The research of this thesis is aligned with the development of NorCPM while also focusing on investigating key mechanisms that give rise to predictability in the Arctic-Atlantic region.
Climate predictions are initialized in different ways, which affects their performance. The first study of the thesis investigates the best initialization method for the Arctic-Atlantic region using NorCPM. Paper I finds that employing a more complex data assimilation method leads to the improved predictive skill of temperature and salinity in the Subpolar North Atlantic (SPNA) but not in the Norwegian Sea. The loss of skill in the Norwegian Sea is found in regions characterized by intense surface heat fluxes and eddy activity, such as the Norwegian and Lofoten Basins. The warm Atlantic water moving northwards from the SPNA to the Norwegian Sea carries thermohaline anomalies, and it is transformed from light-to-dense waters by surface forcing along the path. These two mechanisms are investigated in observation-based data in Paper II. Their relationship is analyzed, focusing on the decadal timescale in the eastern SPNA. Paper II finds that warm anomalies are associated with surface-forced water mass transformation in the light-density classes, while during cold anomalies, more transformation happens in denser classes. This relationship was disrupted during the Great Salinity Anomaly events of the 70s and 90s. Furthermore, the study highlights a faster propagation of thermohaline anomalies in the SPNA compared to the Norwegian Sea, particularly regarding temperature.
The influence of the ocean on the climate of Europe is investigated in Paper III. This study advances the understanding of how constrained ocean variability impacts the atmosphere of NorCPM. The results show a more realistic thermodynamic component of surface air temperature (SAT) over the ocean and some European regions. Paper III shows that there is potential to improve multi-annual to decadal predictions over Europe, which is currently challenging in prediction systems. The research presented in this Thesis enhances the understanding of climate predictability in the Arctic-Atlantic region. It provides insights into the interactions between the atmosphere and ocean and adds to the development of the Norwegian Climate Prediction Model, contributing to making this prediction system operational in the coming years. Following similar approaches as presented in this thesis for other dynamical prediction systems would be highly recommended.

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Forced and internal components of observed Arctic sea-ice changes

Dörr, J.S., Bonan, D.B., Årthun, M., Svendsen, L., Wills, R.C.J. 2023: Forced and internal components of observed Arctic sea-ice changes. The Cryosphere. https://doi.org/10.5194/tc-17-4133-2023

Summary: The Arctic sea-ice cover is strongly influenced by internal variability on decadal timescales, affecting both short-term trends and the timing of the first ice-free summer. Several mechanisms of variability have been proposed, but how these mechanisms manifest both spatially and temporally remains unclear. The relative contribution of internal variability to observed Arctic sea-ice changes also remains poorly quantified. Here, we use a novel technique called low-frequency component analysis to identify the dominant patterns of winter and summer decadal Arctic sea-ice variability in the satellite record. The identified patterns account for most of the observed regional sea-ice variability and trends, and they thus help to disentangle the role of forced and internal sea-ice changes over the satellite record. In particular, we identify a mode of decadal ocean–atmosphere–sea-ice variability, characterized by an anomalous atmospheric circulation over the central Arctic, that accounts for approximately 30 % of the accelerated decline in pan-Arctic summer sea-ice area between 2000 and 2012 but accounts for at most 10 % of the decline since 1979. For winter sea ice, we find that internal variability has dominated decadal trends in the Bering Sea but has contributed less to trends in the Barents and Kara seas. These results, which detail the first purely observation-based estimate of the contribution of internal variability to Arctic sea-ice trends, suggest a lower estimate of the contribution from internal variability than most model-based assessments.

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Supermodeling: improving predictions with an ensemble of interacting models

Schevenhoven , F., Keenlyside, N., Counillon, F., Carrassi, A., Chapman, W.E., Devilliers, M., Gupta, A., Koseki, S., Selten, F., Shen, M.L., Wang, S. 2023: Supermodeling: improving predictions with an ensemble of interacting models. BAMS. https://doi.org/10.1175/BAMS-D-22-0070.1

Summary: The modeling of weather and climate has been a success story. The skill of forecasts continues to improve and model biases continue to decrease. Combining the output of multiple models has further improved forecast skill and reduced biases. But are we exploiting the full capacity of state-of-the-art models in making forecasts and projections? Supermodeling is a recent step forward in the multimodel ensemble approach. Instead of combining model output after the simulations are completed, in a supermodel individual models exchange state information as they run, influencing each other’s behavior. By learning the optimal parameters that determine how models influence each other based on past observations, model errors are reduced at an early stage before they propagate into larger scales and affect other regions and variables. The models synchronize on a common solution that through learning remains closer to the observed evolution. Effectively a new dynamical system has been created, a supermodel, that optimally combines the strengths of the constituent models. The supermodel approach has the potential to rapidly improve current state-of-the-art weather forecasts and climate predictions. In this paper we introduce supermodeling, demonstrate its potential in examples of various complexity, and discuss learning strategies. We conclude with a discussion of remaining challenges for a successful application of supermodeling in the context of state-of-the-art models. The supermodeling approach is not limited to the modeling of weather and climate, but can be applied to improve the prediction capabilities of any complex system, for which a set of different models exists.

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Climate and marine-ecosystem intelligence for a green and competitive Nordic region (policy brief)

Keenlyside, N., Ogilvie A., Yang, S. Koening, T., Counilon F. 2023: Climate and marine-ecosystem intelligence for a green and competitive Nordic region. Nordic Region Fast Track to Vision 2030, NordForsk Policy Brief. https://norden.diva-portal.org/smash/get/diva2:1789341/FULLTEXT03

Summary: Operational climate and marine ecosystem services are urgently needed at the Nordic level. These services are crucial for combating the climate and marine ecosystem emergencies currently threatening the region. They are also needed to manage climate risks and to increase resilience in transport, construction, and food sectors, as well as to develop a renewable energy sector to achieve carbon neutrality. They are important for managing human activities to ensure a healthy marine ecosystem and sustainable fisheries.
We identify two priorities for developing climate and marine-ecosystem services that capitalise on world-leading Nordic research. First, fully integrated climate and marine ecosystems models need to be developed to predict changes on seasonal-to-decadal timescales. Second, services need to be co-developed with a fundamental understanding of societal needs. This requires trans-disciplinary collaboration among climate and ecosystem
researchers, computational scientists, and social scientists, with the active participation of all users.
Cooperation is needed at the Nordic level to address the common challenges that we face. Combining expertise and infrastructure will have major synergistic benefits. The shared cultural and societal values will facilitate the co-development of solutions to achieve a green and more competitive Nordic Region.

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Surface-Forced Variability in the Nordic Seas Overturning Circulation and Overflows

Årthun, M. 2023: Surface-Forced Variability in the Nordic Seas Overturning Circulation and Overflows. Geophys Res Lett. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GL104158

Summary: Water mass transformation in the Nordic Seas and the associated overflow of dense waters across the Greenland-Scotland Ridge (GSR) acts to maintain the lower limb of the Atlantic meridional overturning circulation. Here, we use ocean and atmospheric reanalysis to assess the temporal variability in the Nordic Seas overturning circulation between 1950 and 2020 and its relation to surface buoyancy forcing. We find that variable surface-forced transformation of Atlantic waters in the eastern Nordic Seas can explain variations in overflow transport across the GSR. The production of dense water masses in the Greenland and Iceland Seas is of minor importance to overflow variability. The Nordic Seas overturning circulation shows pronounced multidecadal variability that is in phase with the Atlantic Multidecadal Variability (AMV) index, but no long-term trend. As the AMV is currently transitioning into its negative phase, the next decades could see a decreased overflow from the Nordic Seas.

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Enhancing Seasonal Forecast Skills by Optimally Weighting the Ensemble from Fresh Data

Brajard, J., Counillon, F., Wang, Y., Kimmritz, M. 2023: Enhancing Seasonal Forecast Skills by Optimally Weighting the Ensemble from Fresh Data. Weather and Forecasting. https://doi.org/10.1175/WAF-D-22-0166.1

Summary: Dynamical climate predictions are produced by assimilating observations and running ensemble simulations of Earth system models. This process is time consuming and by the time the forecast is delivered, new observations are already available, making it obsolete from the release date. Moreover, producing such predictions is computationally demanding, and their production frequency is restricted. We tested the potential of a computationally cheap weighting average technique that can continuously adjust such probabilistic forecasts—in between production intervals—using newly available data. The method estimates local positive weights computed with a Bayesian framework, favoring members closer to observations. We tested the approach with the Norwegian Climate Prediction Model (NorCPM), which assimilates monthly sea surface temperature (SST) and hydrographic profiles with the ensemble Kalman filter. By the time the NorCPM forecast is delivered operationally, a week of unused SST data are available. We demonstrate the benefit of our weighting method on retrospective hindcasts. The weighting method greatly enhanced the NorCPM hindcast skill compared to the standard equal weight approach up to a 2-month lead time (global correlation of 0.71 vs 0.55 at a 1-month lead time and 0.51 vs 0.45 at a 2-month lead time). The skill at a 1-month lead time is comparable to the accuracy of the EnKF analysis. We also show that weights determined using SST data can be used to improve the skill of other quantities, such as the sea ice extent. Our approach can provide a continuous forecast between the intermittent forecast production cycle and be extended to other independent datasets.

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Key physical processes and their model representation for projecting climate impacts on subarctic Atlantic net primary production: A synthesis

Myksvoll, M. S., Sandø, A. B., Tjiputra, J., Samuelsen, A., Çağlar Yumruktepe, V., Li, C., Mousing, E. A., Bettencourt, J.P.H., Ottersen, G. 2023: Key physical processes and their model representation for projecting climate impacts on subarctic atlantic net primary production: A synthesis. Progress in Oceanography. https://doi.org/10.1016/j.pocean.2023.103084

Summary: Oceanic net primary production forms the foundation of marine ecosystems. Understanding the impact of climate change on primary production is therefore critical and we rely on Earth System Models to project future changes. Stemming from their use of different physical dynamics and biogeochemical processes, these models yield a large spread in long-term projections of change on both the global and regional scale. Here we review the key physical processes and biogeochemical parameterizations that influence the estimation of primary production in Earth System Models and synthesize the available projections of productivity in the subarctic regions of the North Atlantic. The key processes and modelling issues we focus on are mixed layer depth dynamics, model resolution and the complexity and parameterization of biogeochemistry. From the model mean of five CMIP6 models, we found a large increase in PP in areas where the sea ice retreats throughout the 21st century. Stronger stratification and declining MLD in the Nordic Seas, caused by sea ice loss and regional freshening, reduce the vertical flux of nutrients into the photic zone. Following the synthesis of the primary production among the CMIP6 models, we recommend a number of measures: constraining model hindcasts through the assimilation of high-quality long-term observational records to improve physical and biogeochemical parameterizations in models, developing better parameterizations for the sub-grid scale processes, enhancing the model resolution, downscaling and multi-model comparison exercises for improved regional projections of primary production.

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