The cod has followed the thermometer

In recent decades the cod stock in the Barents Sea has gone up and down with the ocean temperature. Future development depends on more than the water.

The researchers found a statistical correlation between sea temperature, zooplankton and cod during the last decades.

They then used this relationship to estimate how the cod population may be expected to develop with different degrees of CO2 emissions and temperature rise in this century. The scenarios for the future climate were taken from climate models.

The researchers made predictions and projections for the biomass of cod in the Barents Sea, both for the coming decades and by the end of the century.

Read the article: The cod has followed the thermometer

 

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.

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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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Correcting errors in seasonal Arctic sea ice prediction of Earth system models with machine learning

He, Z, Wang, Y, Brajard, J, Wang, X, and Shen, Z 2025: Correcting errors in seasonal Arctic sea ice prediction of Earth system models with machine learning. The Cryosphere. https://doi.org/10.5194/tc-19-3279-2025

Summary: While Earth system models are essential for seasonal Arctic sea ice prediction, they often exhibit significant errors that are challenging to correct. In this study, we integrate a multilayer perceptron (MLP) machine learning (ML) model into the Norwegian Climate Prediction Model (NorCPM) to improve seasonal sea ice predictions. We compare the online and offline error correction approaches. In the online approach, ML corrects errors in the model’s instantaneous state during the model simulation, while in the offline approach, ML post-processes and calibrates predictions after the model simulation. Our results show that the ML models effectively learn and correct dynamical model errors in both approaches, leading to improved predictions of Arctic sea ice during the test period (i.e., 2003–2021). Both approaches yield the most significant improvements in the marginal ice zone, where error reductions in sea ice concentration exceed 20 %. These improvements vary seasonally, with the most substantial enhancements occurring in the Atlantic, Siberian, and Pacific regions from September to January. The offline error correction approach consistently outperforms the online error correction approach. This is primarily because the online approach targets only instantaneous model errors on the 15th of each month, while errors can grow during the subsequent 1-month model integration due to interactions among the model components, damping the error correction in monthly averages. Notably, in September, the online approach reduces the error of the pan-Arctic sea ice extent by 50 %, while the offline approach achieves a 75 % error reduction.

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Improving Ocean Reanalysis with the Offline Ensemble Kalman Smoother

Wang, Y., Counillon, F., Ying, Y., Barthélémy, S., Evensen, G. 2025: Improving Ocean Reanalysis with the Offline Ensemble Kalman Smoother. Tellus A. https://doi.org/10.16993/tellusa.4087

Summary: The Ensemble Kalman Smoother (EnKS), an extension of the Ensemble Kalman Filter, can improve the accuracy of the state estimate by assimilating ‘future’ observations. We propose to use the EnKS algorithm to enhance the accuracy and reliability of preexisting reanalyses produced with a fully coupled Earth system model. The offline EnKS is applied to two reanalyses of the Norwegian Climate Prediction Model (NorCPM) to update sea surface height, mixed layer depth, and temperature and salinity for all depth levels of the reanalyses. In an idealized framework, we tune temporal localization parameters and reveal that the optimal temporal localization parameter is 0.1, corresponding to a time delay of about 13 days. In a real framework, we find that observation error variance has to be inflated by a factor of four to account for the autocorrelation of the gridded observational product and avoid overfitting. In both frameworks, the offline EnKS improves the accuracy for the top 300 m temperature, sea surface height, and mixed layer depth, but yields limited improvements in the top 300 m salinity and the water properties below 300 m. Also, it enhances the reliability of the reanalysis. The improvement is notably lower in a real framework than in an idealized framework; this is mostly due to the lack of high quality and independent datasets for proper validation. Overall, this study demonstrates that the offline EnKS has the potential for efficiently improving pre-existing reanalyses.

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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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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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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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The Nordic Seas overturning is modulated by northward-propagating thermohaline anomalies

Chafik, L., Årthun, M., Langehaug, H.R., Nilsson, J., Rossby, T. 2025: The Nordic Seas overturning is modulated by northward-propagating thermohaline anomalies. Commun Earth Environ. https://doi.org/10.1038/s43247-025-02557-x

Summary: The inflow of warm waters into the Nordic Seas, crucial for sustaining the climate-regulating Atlantic overturning circulation, can be reconstructed from hydrography using a north-south dynamic height gradient across the Greenland-Scotland Ridge. Variations in this influx are herein linked to northward-propagating thermohaline anomalies, initially observed at the intergyre boundary and likely driven by changes in ocean heat transport. As these anomalies reach the eastern subpolar North Atlantic, they modulate the cross-ridge dynamic height difference, thereby influencing both the Atlantic inflow and the Nordic Seas overflows on multi-year to decadal scales. Thus, these thermohaline anomalies play a dynamically active role in modulating the watermass exchanges across the ridge and downstream along the Atlantic Water path, rather than being a simple passive train of signals. This explains why these thermohaline signals are a key source of climate predictability and provides fresh insights into the functioning of the Nordic Seas overturning circulation from observations.

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