Tag: marikok

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

 

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.

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

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.

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

Prediction of the Northeast Arctic cod biomass in the Barents Sea

Mariko Koseki joined BCPU as an intern in autumn 2021. Mariko has a Masters in Environmental Science which she obtained at Hokkaido University in Japan.

“I, Mariko Koseki, am an intern within BCPU, and I have been working with Dr. Anne Britt Sandø at the Institute of Marine Research since autumn of 2021.
During the internship, we have focused on the Northeast Arctic cod (NEA cod/Gadus Morhua) biomass in the Barents Sea and developed regression models to predict variations in cod biomass in the future.
The NEA cod is one of the most important species in the Barents Sea for both the ecosystem and as a commercial stock. Several earlier studies reported that the recent warming condition in the Barents Sea has led to high cod biomass.
To construct regression models for total stock biomass of the NEA cod, we used hydrographic and biological variables, such as temperature, salinity, sea ice fraction, primary- and secondary production as explanatory variables. These variables were obtained from hindcast simulations with regional ocean and ecosystem models. Finally, we used the same regression models with variables from downscaled climate scenarios to project future variations in the NEA cod.
We found that several of the regression models have high prediction skills and captured the variations in total stock biomass in the Barents Sea well. Moreover, based on downscaled climate projections, we made maps of spatial distributions of cod biomass in the future. However, errors between observations and predictions of cod biomass necessitate further improvement of the regression models. Now we are preparing to publish this study as a scientific article.
I would like to thank everyone who has supported my internship, and I hope to make use of my experience in my next steps.” – Mariko