Tag: keenlyside

Bringing Climate Models to Everyone

How AI is making complex data understandable

Climate models can be a challenge to understand, especially for those who don’t have an education in or work with climate science. So, how do you present findings to those who could use the information, but can’t decipher the complicated data from climate models?

That has been part of the work for the NorCPM-team, who have developed an app with an interactive map of the earth, supported by an AI-system that can explain the information to users.

Read the article: Bringing Climate Models to Everyone

 

Implementation and validation of a supermodeling framework into Community Earth System Model version 2.1.5

Chapman, W. E., F. Schevenhoven, J. Berner, N. Keenlyside, I. Bethke, P.-G. Chiu, A. Gupta, and J. Nusbaumer 2025: Implementation and validation of a supermodelling framework into CESM version 2.1.5. Geosci. Model Dev.. https://doi.org/10.5194/gmd-18-5451-2025

Summary: Here we present a research framework for the first atmosphere-connected supermodel using state-of-the-art atmospheric models. The Community Atmosphere Model (CAM) versions 5 and 6 exchange information interactively while running, a process known as supermodeling. The primary goal of this approach is to synchronize the models, allowing them to create a new dynamical system which can theoretically benefit from each component model, in part by increasing the dimensionality of the system.

In this study, we examine a single untrained supermodel where each model version is equally weighted in creating pseudo-observations. We demonstrate that the models synchronize well without decreased variability, particularly in storm track regions, across multiple timescales, and for variables where no information has been exchanged. Synchronization is less pronounced in the tropics, and in regions of lesser synchronization we observe a decrease in high-frequency variability. Additionally, the low-frequency modes of variability (North Atlantic Oscillation and Pacific North American Pattern) are not degraded compared to the base models. For some variables, the mean bias, as well as the non-interactive ensemble mean, is reduced compared to control simulations of each model version.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

An ensemble-based coupled reanalysis of the climate from 1860 to the present (CoRea1860+)

Wang, Y., Counillon, F., Svendsen, L., Chiu, P.-G., Keenlyside, N., Laloyaux, P., Koseki, M., and de Boisseson, E. 2025: An ensemble-based coupled reanalysis of the climate from 1860 to the present (CoRea1860+). Earth Syst. Sci. Data. https://doi.org/10.5194/essd-17-4185-2025

Summary: Climate reanalyses are essential for studying climate variability, understanding climate processes, and initializing climate predictions. We present CoRea1860+ (Wang and Counillon, 2025, https://doi.org/10.11582/2025.00009), a 30-member coupled reanalysis spanning from 1860 to the present, produced using the Norwegian Climate Prediction Model (NorCPM) and assimilating sea surface temperature (SST) observations. NorCPM combines the Norwegian Earth System Model with the ensemble Kalman filter data assimilation method. SST, available throughout the entire period, serves as the primary source of instrumental oceanic measurements prior to the 1950s. CoRea1860+ belongs to the category of sparse-input reanalyses, designed to minimize artefacts arising from changes in the observation network over time. By exclusively assimilating oceanic data, this reanalysis offers valuable insights into the ocean’s role in driving climate system variability, including its influence on the atmosphere and sea ice. This study first describes the numerical model, the SST dataset, and the assimilation implementation used to produce CoRea1860+. It then provides a comprehensive evaluation of the reanalysis across four key aspects, namely reliability, ocean variability, sea ice variability, and atmospheric variability, benchmarked against more than 10 independent reanalyses and observational datasets. Overall, CoRea1860+ demonstrates strong reliability, particularly in observation-rich periods, and provides a reasonable representation of climate variability. It successfully captures key features such as multi-decadal variability and long-term trends in ocean heat content, the Atlantic meridional overturning circulation, and sea ice variability in both hemispheres. Furthermore, to some extent, CoRea1860+ agrees with the reference atmospheric datasets for surface air temperature, precipitation, sea level pressure, and 500 hPa geopotential height, especially in the tropics where air–sea interactions are most pronounced.

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Climate impacts of the El Niño–Southern Oscillation in Africa

Cai, W., Reason, C., Mohino, E., …. , N.S. Keenlyside et al.  2025: Climate impacts of the El Niño–Southern Oscillation in Africa. Nat Rev Earth Environ. https://doi.org/10.1038/s43017-025-00705-7

Summary: The El Niño–Southern Oscillation (ENSO) — describing shifts between warm El Niño and cold La Niña phases — has a substantial effect on the global climate. In this Review, we outline the mechanisms and climate impacts of ENSO in Africa, focusing on rainfall. ENSO’s influence varies strongly by season, region, phase, event and decade, highlighting complex dynamics and asymmetries. Although difficult to generalize, key characteristics include: anomalies across the Sahel in July–September, related to the tropospheric temperature mechanism; a strong dipole in anomalies between eastern and southern Africa during October–December (the short rain reason) and December–February, linked to interactions with the Indian Ocean Dipole and Indian Ocean Basin mode, respectively; and anomalies over southern Africa (with possible indications of opposite anomalies over East Africa) during March–May (the long rain season), associated with continuation of the Indian Ocean Basin mode. These teleconnections tend to be most pronounced for East Pacific El Niño and Central Pacific La Niña events, as well as during decades when interbasin interactions are strongest. Although challenging to simulate, climate models suggest that these impacts will strengthen in the future, manifesting as an increased frequency of ENSO-related dry and wet extremes. Given the reliance of much of Africa on rain-fed agriculture, resolving these relationships is vital, necessitating realistic simulation of regional circulations, ENSO and its interbasin interactions.

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Unraveling the Complexity of Global Climate Dynamics: Interactions among El Niño–Southern Oscillation, Atlantic Meridional Overturning Circulation, and Tropical Basins Across Different Timescales

Hu, A., I. Richter, Y. Okumura, N. Burls, N. Keenlyside, R. Parfitt, K. Bellomo, A. Bellucci, R. Farneti, A. Fedorov, B. S. Ferster, C. He, Q. Li, D. Matei 2025: Unraveling the Complexity of Global Climate Dynamics: Interactions among El Niño–Southern Oscillation, Atlantic Meridional Overturning Circulation, and Tropical Basins Across Different Timescales. Ocean-Land-Atmos Res.. https://spj.science.org/doi/10.34133/olar.0096

Summary: Tropical basin interactions and the climatic linkages between mid-to-high latitudes and the tropics are active research areas. These interactions include the influence of El Niño–Southern Oscillation (ENSO) on the tropical Indian and Atlantic oceans, the feedback from these basins on ENSO, the influence of the tropics on mid-to-high-latitude climates, and the feedback from higher latitudes on tropical climate variability. This review summarizes the current understanding of these relationships and key underlying physical processes. In particular, we assessed the current knowledge of tropical variability and the interactions between the tropics and extratropics, including ENSO variability and diversity, the influence of ENSO on the tropical Atlantic and Indian Oceans, interactions among tropical basins on different timescales, variability in the Atlantic meridional overturning circulation (AMOC), the effect of tropical basins on the AMOC, the relationship between the AMOC and Atlantic multidecadal variability, the influence of the AMOC on ENSO and tropical variability, and the impact of other mid-to-high-latitude processes on tropical variability. Although ENSO is the dominant mode of variability on interannual timescales, its characteristics are not stationary and can be influenced by processes from other tropical basins and mid-to-high latitudes. The strength and variations of these interactions among different tropical basins and latitudes can be modulated by changes in external forcing, whether of natural or anthropogenic origin, and may also be shaped by nonlinear interactions between different modes of internal variability.

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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.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.