Exploration of short-term predictions and long-term projections of Barents Sea cod biomass using statistical methods on data from dynamical models

Koseki M, Sandø AB, Ottersen G, Årthun M, Stiansen JE 2025: Exploration of short-term predictions and long-term projections of Barents Sea cod biomass using statistical methods on data from dynamical models. PLoS One. https://doi.org/10.1371/journal.pone.0328762

Summary: This study aims to explore how well simple statistical modeling can generate short-term predictions and long-term projections of the total biomass of the Northeast Arctic stock of Atlantic cod (Gadus Morhua) inhabiting the Barents Sea. We examine the predictability of statistical models only based on hydrographic and lower trophic level biological variables from dynamical modeling. Simple and multiple linear regression models are developed based on gridded variables from the regional ocean model NEMO-NAA10km and the ecosystem model NORWECOM.E2E. This includes the essential environmental variables temperature, salinity, sea ice concentration, primary production and secondary production. The regression models are statistically evaluated to find variables that can capture variability in Barents Sea cod biomass. Finally, future total cod stock biomass is projected by applying the best found regression models to the range of downscaled IPCC climate scenarios from the coupled Intercomparison Project Phase 6 (CMIP6 Shared Socioeconomic Pathways; SSP1–2.6, SSP2–4.5, SSP5–8.5). Our prediction models are based on variables that affect cod both directly and indirectly. We find that several regression models have high prediction skill and capture the variations in total stock biomass of the Northeast Arctic cod well. Our results suggest that increased ocean temperature and abundant zooplankton may lead to a large cod stock. However, even if total stock biomass has a positive trend with an increase in copepods in the highest warming scenario SSP5–8.5, we found that it has a negative trend in the low emission scenario SSP1–2.6 when the regional ocean and ecosystem models show weak cooling and reduced zooplankton. We show that variability in essential environmental variables can provide a remarkably good first approximation to cod dynamics. However, to resolve the full picture other factors like fishing and natural mortality also need to be addressed explicitly.

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Predicting Atlantic and Benguela Niño events with deep learning

Bachèlery ML, Brajard J, Patacchiola M, Illig S, Keenlyside N 2025: Predicting Atlantic and Benguela Niño events with deep learning. Sci. Adv.. https://doi.org/10.1126/sciadv.ads5185

Summary: Atlantic and Benguela Niño events substantially affect the tropical Atlantic region, with far-reaching consequences on local marine ecosystems, African climates, and El Niño Southern Oscillation. While accurate forecasts of these events are invaluable, state-of-the-art dynamic forecasting systems have shown limited predictive capabilities. Thus, the extent to which the tropical Atlantic variability is predictable remains an open question. This study explores the potential of deep learning in this context. Using a simple convolutional neural network architecture, we show that Atlantic/Benguela Niños can be predicted up to 3 to 4 months ahead. Our model excels in forecasting peak-season events with remarkable accuracy extending lead time to 5 months. Detailed analysis reveals our model’s ability to exploit known physical precursors, such as long-wave ocean dynamics, for accurate predictions of these events. This study challenges the perception that the tropical Atlantic is unpredictable and highlights deep learning’s potential to advance our understanding and forecasting of critical climate events.

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Evaluation of the effects of Argo data quality control on global ocean data assimilation systems

Ishikawa I, Fujii Y, de Boisseson E, Wang Y and Zuo H 2024: Evaluation of the effects of Argo data quality control on global ocean data assimilation systems. Front Mar Sci. https://doi.org/10.3389/fmars.2024.1496409

Summary: A series of observing system experiments (OSEs) were conducted in order to evaluate the effects of Argo data quality control (QC), by using the three global ocean data assimilation systems. During the experimental period between 2015 and 2020, some Argo floats are affected by the abrupt salinity drifts, which caused spurious increasing trend of the global mean salinity in the reanalyses using the observations with only real-time QC applied. The spurious trend is mitigated by applying the gray list provided by the Argo Global Data Assembly Centres (GDAC), and further reduced by assimilating the delayed-mode Argo data of the Argo GDAC instead of the real-time Argo data. These impacts of the Argo QC are generally consistent among the three ocean data assimilation systems. Further investigations in the JMA’s system show that errors in the analyzed salinity with respect to the delayed-mode Argo data are smaller in the OSE with more rigorous QC, and the spatiotemporal variations in the sea-surface dynamic height are reproduced better. Additionally, QC impacts on the analyzed temperatures are shown not to directly reflect the difference in temperature observations among OSEs, and may be affected by difference in the salinity observations among OSEs through the cross-covariance relationship in the data-assimilation systems.

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Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation

Nair, A.S., Counillon, F., Keenlyside, N. 2024: Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation. Clim Dyn. https://doi.org/10.1007/s00382-024-07444-3

Summary: This study shows the importance of soil moisture (SM) in subseasonal-to-seasonal (S2S) predictions at mid-latitudes. We do this through introducing the Norwegian Climate Prediction Model Land (NorCPM-Land), a land reanalysis framework tailored for integration with the Norwegian Climate Prediction Model (NorCPM). NorCPM-Land assimilates blended SM data from the European Space Agency’s Climate Change Initiative into a 30-member offline simulation of the Community Land Model with fluxes from the coupled model. The assimilation of SM data reduces error in SM by 10.5 % when validated against independent SM observations. It also improves latent heat flux estimates, illustrating that the adjustment of underlying SM significantly augments the capacity to model land surface dynamics. We evaluate the added value of land initialisation for subseasonal predictions, by comparing the performance of hindcasts (retrospective prediction) using the standard NorCPM with a version where the land initial condition is taken from NorCPM-Land reanalysis. The hindcast covers the period 2000 to 2019 with four start dates per year. Land initialisation enhances SM predictions, reducing error by up to 2.5-month lead time. Likewise, the error for precipitation and temperature shows improvement up to a lead time of 1.5-month. The largest improvements are observed in regions with significant land-atmospheric coupling, such as the Central United States, the Sahel, and Central India. This method further enhances the prediction of extreme temperature variations, both high and low, with the most notable improvements seen in regions at mid and high latitudes, including parts of Europe, the United States, and Asia. Overall, our study provides further evidence for the significant role of SM content in enhancing the accuracy of subseasonal predictions. This study introduces a technique for improved land initialisation, utilising the same model employed in climate predictions.

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Coupled data assimilation for climate prediction: a focus on ocean-atmosphere coupling (PhD thesis)

Lilian Carolina Garcia Oliva (2024-10-17): Coupled data assimilation for climate prediction: a focus on ocean-atmosphere coupling. PhD thesis, University of Bergen, Bergen, Norway. https://hdl.handle.net/11250/3157446

Summary: Seasonal-to-Decadal (S2D) climate predictions can provide decision-making information for diverse sectors, such as food security, energy and climate adaptation. The initial condition of the ocean is fundamental for providing skilful S2D predictions. A method to estimate the ocean’s initial condition is by merging the model and observations through a process called Coupled Data Assimilation (CDA). Ocean observations have demonstrated their potential to achieve skilful prediction. The Norwegian Climate Prediction Model (NorCPM) features an advanced Ocean Data Assimilation (ODA) scheme based on an ensemble method. This thesis outlines our efforts to improve S2D predictions within the NorCPM using atmospheric observations.

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Hybrid covariance super-resolution data assimilation

Barthélémy, S., Counillon, F., Brajard, J., Bertino, L. 2024: Hybrid covariance super-resolution data assimilation. Ocean Dynamics. https://doi.org/10.1007/s10236-024-01643-6

Summary: The super-resolution data assimilation (SRDA) enhances a low-resolution (LR) model with a Neural Network (NN) that has learned the differences between high and low-resolution models offline and performs data assimilation in high-resolution (HR). The method enhances the accuracy of the EnKF-LR system for a minor computational overhead. However, performance quickly saturates when the ensemble size is increased due to the error introduced by the NN. We therefore combine the SRDA with the mixed-resolution data assimilation method (MRDA) into a method called “Hybrid covariance super-resolution data assimilation” (Hybrid SRDA). The forecast step runs an ensemble at two resolutions (high and low). The assimilation is done in the HR space by performing super-resolution on the LR members with the NN. The assimilation uses the hybrid covariance that combines the emulated and dynamical HR members. The scheme is extensively tested with a quasi-geostrophic model in twin experiments, with the LR grid being twice coarser than the HR. The Hybrid SRDA outperforms the SRDA, the MRDA, and the EnKF-HR at a given computational cost. The benefit is the largest compared to the EnKF-HR for small ensembles. However, even with larger computational resources, using a mix of high and low-resolution members is worth it. Besides, the Hybrid SRDA, the EnKF-HR, and the SRDA, unlike the MRDA, prevent the smoothing of dynamical structures of the background error covariance matrix. The Hybrid SRDA method is also attractive because it is customizable to available resources.

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Decadal prediction centers prepare for a major volcanic eruption

Sospedra-Alfonso, R., Merryfield, W.J., Toohey, M., Timmreck, C., Vernier, J-P., Bethke, I., Wang, Y., Bilbao, R., Donat, M.G., Ortega, P., Cole, J., Lee, W.-S., Delworth, T.L., Paynter, D., Zeng, F., Zhang, L., Khodri, M., Mignot, J., Swingedouw, D., Torres, O., Hu, S., Man, W., Zuo, M., Hermanson, L., Smith, D., Kataoka, T., Tatebe, H. 2024: Decadal prediction centers prepare for a major volcanic eruption. Bulletin of the American Meteorological Society. https://doi.org/10.1175/BAMS-D-23-0111.1

Summary: The World Meteorological Organization’s Lead Centre for Annual-to-Decadal Climate prediction issues operational forecasts annually as guidance for regional climate centers, climate outlook
forums and national meteorological and hydrological services. The occurrence of a large volcanic eruption such as that of Mount Pinatubo in 1991, however, would invalidate these forecasts and prompt producers to modify their predictions. To assist and prepare decadal prediction centers for this eventuality, the Volcanic Response activities under the World Climate Research Programme’s Stratosphere-troposphere Processes And their Role in Climate (SPARC) and the Decadal Climate Prediction Project (DCPP) organized a community exercise to respond to a hypothetical large eruption occurring in April 2022. As part of this exercise, the Easy Volcanic Aerosol forcing generator was used to provide stratospheric sulfate aerosol optical properties customized to the configurations of individual decadal prediction models. Participating centers then reran forecasts for 2022-2026 from their original initialization dates and in most cases also from just before the eruption at the beginning of April 2022, according to two candidate response protocols. This article describes various aspects of this SPARC/DCPP Volcanic Response Readiness Exercise (VolRes-RE), including the hypothesized volcanic event, the modified forecasts under the two protocols from the eight contributing centers, the lessons learned during the coordination and execution of this exercise, and the recommendations to the decadal prediction community for the response to an actual eruption.

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