Warming and freshening coastal waters impact harmful algal bloom frequency in high latitudes

Silva, E., Counillon, F., Brajard, J., Davy, R., Outten, S., Pettersson, L.H. and Keenlyside, N. 2025: Warming and freshening coastal waters impact harmful algal bloom frequency in high latitudes. Commun Earth Environ. https://doi.org/10.1038/s43247-025-02421-y

Summary: Harmful algal blooms contaminate seafood with toxins and poison humans and wildlife upon consumption. Toxic algae niches are projected to expand in high latitudes, but how the frequency of their blooms will evolve is still little known. Here we use climate models, 14 years of observations and probabilistic models of toxic algae, to assess the frequency of harmful algal blooms in a future warmer world. The warmer ocean temperatures increase the blooms in spring and autumn. However, the blooms reduce in summer as surface waters become excessively warm. Freshening reduces the blooms of species confined to high salinity ranges and has no effect on increasing the blooms. In a 3 °C warmer world, the blooms of D. acuta might increase by 50% and A. tamarense complex reduce by 40% along the Norwegian coast. Therefore, humans and wildlife are likely to become more exposed to diarrheic toxins and less to paralytic toxins.

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The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)

Richter, I., Chang, P., Chiu, P.-G., Danabasoglu, G., Doi, T., Dommenget, D., Gastineau, G., Gillett, Z. E., Hu, A., Kataoka, T., Keenlyside, N. S., Kucharski, F., Okumura, Y. M., Park, W., Stuecker, M. F., Taschetto, A. S., Wang, C., Yeager, S. G., and Yeh, S.-W. 2025: The Tropical Basin Interaction Model Intercomparison Project (TBIMIP). Geosci. Model Dev.. https://doi.org/10.5194/gmd-18-2587-2025

Summary: Large-scale interaction between the three tropical ocean basins is an area of intense research that is often conducted through experimentation with numerical models. A common problem is that modeling groups use different experimental setups, which makes it difficult to compare results and delineate the role of model biases from differences in experimental setups. To address this issue, an experimental protocol for examining interaction between the tropical basins is introduced. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) consists of experiments in which sea surface temperatures (SSTs) are prescribed to follow observed values in selected basins. There are two types of experiments. One type, called standard pacemaker, consists of simulations in which SSTs are restored to observations in selected basins during a historical simulation. The other type, called pacemaker hindcast, consists of seasonal hindcast simulations in which SSTs are restored to observations during 12-month forecast periods. TBIMIP is coordinated by the Climate and Ocean – Variability, Predictability, and Change (CLIVAR) Research Focus on Tropical Basin Interaction. The datasets from the model simulations will be made available to the community to facilitate and stimulate research on tropical basin interaction and its role in seasonal-to-decadal variability and climate change.

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Dominant modes of interannual winter SAT covariability between the Arctic and the Tibetan Plateau: spatio-temporal structures and dynamical linkages

Sun, X., Y. Gao, X.-Q. Yang, Z. Fang, X. Zhang, S. Yuan, N. S. Keenlyside 2025: Dominant modes of interannual winter SAT covariability between the Arctic and the Tibetan Plateau: spatio-temporal structures and dynamical linkages. Clim Dyn. https://doi.org/10.1007/s00382-025-07711-x

Summary: As two highly sensitive climate zones in the world, the Arctic and Tibetan Plateau (TP) regions respectively exhibit significantly uneven spatial variability in surface air temperature (SAT) and greatly influence the Eurasian climate on the interannual timescale. However, despite the synchronized warming trends in these two regions, their interannual spatio-temporal connection remains unclear. In this study, we applied the singular value decomposition (SVD) method to ERA5 wintertime surface air temperature anomalies to explore the dominant modes of SAT covariability between the Arctic and TP. We identified two major interannual modes: the dipolar Arctic-uniform TP (DA-UTP) and the quadrupolar Arctic-dipolar TP (QA-DTP), which together explain 82% of their covariance. The DA-UTP mode resembles the negative phase of the Arctic Oscillation, characterized by a hemispheric-scale pattern of “warm northern North America—cold northern Eurasia—warm TP”, while the QA-DTP mode exhibits a meridional teleconnection in the eastern hemisphere, featuring “warm Barents and Kara Seas—cold Eurasia—warm southern TP”. Both modes primarily draw energy from the North Atlantic Ocean and affect East Asian through the atmospheric Rossby wave train. The corresponding North Atlantic SST anomalies display a tripolar distribution, with the center of the negative SST gradient anomaly in the second mode shifted southward compared to the first. These two climate modes further modulate synoptic and sub-seasonal-to-seasonal winter temperature anomalies in Eurasia by altering the hemispheric-scale temperature gradient. The findings of this study contribute to a deeper knowledge and understanding of the interannual spatial and temporal relationships of wintertime surface temperature anomalies between the Arctic and TP.

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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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Amplified wintertime Arctic warming causes Eurasian cooling via nonlinear feedback of suppressed synoptic eddy activities

Yin, M.,  X.-Q. Yang, L. Sun, L. Tao, N. Keenlyside 2025: Amplified wintertime Arctic warming causes Eurasian cooling via nonlinear feedback of suppressed synoptic eddy activities. Sci. Adv.. https://www.science.org/doi/10.1126/sciadv.adr6336

Summary: The amplified wintertime Arctic warming has accelerated in recent decades. However, whether and how the warming can cause Eurasian cooling remains debated. By identifying daily Arctic warming events, we find direct observational evidence that the Arctic warming tends to cause substantial Eurasian cooling and an increase in occurrence frequency of Eurasian cooling events with a roughly 2-day lag. We propose a mechanism explaining the causality. We find that the Arctic warming causes a large suppression in activities of daily weather disturbances (referred to as synoptic eddies) over high-latitude Eurasia. This produces a meridional dipole in geopotential height anomalies characterized by an equivalent-barotropic anomalous low (high) and a lower-level cooling (warming) over mid-latitude Eurasia (the Arctic) via a nonlinear eddy–to–mean flow feedback. The feedback induces near-surface northeasterly anomalies that enlarge the Eurasian cooling via cold advection. Thus, we conclude that the warm Arctic versus cold Eurasia is essentially an intrinsic dipole determined by synoptic eddy–mean flow interaction.

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A novel ensemble-based parameter estimation for improving ocean biogeochemistry in an Earth system model

Singh, T., Counillon, F., Tjiputra, J., Wang, Y. 2025: A novel ensemble-based parameter estimation for improving ocean biogeochemistry in an Earth system model. JAMES. https://doi.org/10.1029/2024MS004237

Summary: Estimating ocean biogeochemistry (BGC) parameters in Earth System Models is challenging due to multiple error sources and interlinked parameter sensitivities. Reducing the temperature and salinity bias in the ocean physical component of the Norwegian Earth System Model (NorESM) diminishes the BGC state bias at intermediate depth but leads to a greater bias increase near the surface. This suggests that BGC parameters are tuned to compensate for the physical ocean model biases. We successfully apply the iterative ensemble smoother data assimilation technique to estimate BGC parameters in NorESM with reduced bias in its physical ocean component. We estimate BGC parameters based on the monthly climatological error of nitrate, phosphate, and oxygen in a coupled reanalysis of NorESM that assimilates observed monthly climatology of temperature and salinity. First, we compare the performance of globally uniform and spatially varying parameter estimations. Both approaches reduce BGC bias obtained with default parameters, even for variables not assimilated in the parameter estimation (e.g., CO2 fluxes and primary production). While spatial parameter estimation performs locally best, it also increases biases in areas with few observations, and overall performs poorer than global parameter estimation. A second iteration further reduces the bias in the near-surface BGC with global parameter estimation. Finally, we assess the performance of global estimated parameters in a 30-year coupled reanalysis produced by assimilating time-varying temperature and salinity observations. This reanalysis reduces error by 10%–20% for phosphate, nitrate, oxygen, and dissolved inorganic carbon compared to a reanalysis done with default parameters.

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Impact of Ocean, Sea Ice or Atmosphere Initialization on Seasonal Prediction of Regional Antarctic Sea Ice

Xiu, Y., Wang, Y., Luo, H., Garcia-Oliva, L., Yang, Q. 2025: Impact of ocean, sea ice or atmosphere initialization on seasonal prediction of regional Antarctic sea ice. JAMES. https://doi.org/10.1029/2024MS004382

Summary: This study investigates how the atmosphere, ocean, or sea ice observations affect the seasonal prediction of Antarctic sea ice. We analyze three sets of predictions from the Norwegian Climate Prediction Model, each integrating different data sets of the atmosphere, ocean, or sea ice. Initially, we assess the seasonal cycles, trends, and variability of Antarctic sea ice in these data sets. We found that including atmosphere observations gave the best seasonal cycle compared to the observed sea ice. However, the linear trend in sea ice when including atmospheric data is poorly reproduced in the western Southern Ocean. Regarding variability, including the combined ocean and sea ice data gave the best performance. Next, we assess the accuracy of regional Antarctic sea ice prediction. We found that the accuracy varies with region and season. Austral winter predictions in western Antarctic have some skill up to a year in advance, while those in the eastern Antarctic are less reliable. Predictions based on atmosphere data are generally more accurate than those based on ocean or ocean/sea-ice data, especially when predicting from July or October. Interestingly, once ocean data is used, involving additional sea ice data improves sea ice concentration in the reanalysis but not in the predictions.

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