Predicting the temperature of the Barents Sea

Knowledge of the influence of the physical environment on commercially important fish stocks in the North Atlantic has increased during the last decade. To allow this information to be used in fisheries management, some forecast of the environment is important. Predictions of temperature in the Arct...

Full description

Bibliographic Details
Published in:Fisheries Oceanography
Main Authors: Ottersen, Geir, Ådlandsvik, Bjørn, Loeng, Harald
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2000
Subjects:
Online Access:http://dx.doi.org/10.1046/j.1365-2419.2000.00127.x
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1046%2Fj.1365-2419.2000.00127.x
https://onlinelibrary.wiley.com/doi/pdf/10.1046/j.1365-2419.2000.00127.x
id crwiley:10.1046/j.1365-2419.2000.00127.x
record_format openpolar
spelling crwiley:10.1046/j.1365-2419.2000.00127.x 2024-06-02T08:04:06+00:00 Predicting the temperature of the Barents Sea Ottersen, Geir Ådlandsvik, Bjørn Loeng, Harald 2000 http://dx.doi.org/10.1046/j.1365-2419.2000.00127.x https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1046%2Fj.1365-2419.2000.00127.x https://onlinelibrary.wiley.com/doi/pdf/10.1046/j.1365-2419.2000.00127.x en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Fisheries Oceanography volume 9, issue 2, page 121-135 ISSN 1054-6006 1365-2419 journal-article 2000 crwiley https://doi.org/10.1046/j.1365-2419.2000.00127.x 2024-05-03T11:15:10Z Knowledge of the influence of the physical environment on commercially important fish stocks in the North Atlantic has increased during the last decade. To allow this information to be used in fisheries management, some forecast of the environment is important. Predictions of temperature in the Arcto‐boreal Barents Sea have been given for many years, both as subjective opinions of scientists and implicitly in stock assessment assumptions of, e.g., mortality rates. To evaluate an objective statistical forecasting system, we have analysed time series representing mechanisms previously proposed as influencing the temperature of the Barents Sea. These include components of suggested periodic nature, large‐scale advective effects, regional processes, and atmospheric teleconnections. The predictability of Barents Sea temperature based on the above mechanisms was evaluated through calculations of auto‐ and cross‐correlations, linear regression, spectral analysis and autoregressive modelling. Forecasts based on periodic fluctuations in temperature performed poorly. Advection alone did not explain a major part of the variability. The precision of predictions six months ahead varied with season; forecasts from spring to autumn had least uncertainty. A first‐order autoregressive model, including modelled atmospherically driven volume flux to the western Barents Sea during the preceding year and the position of the Gulf Stream off the eastern coast of the USA two years earlier, explained 50% of the total historical temperature variability. Article in Journal/Newspaper Barents Sea North Atlantic Wiley Online Library Barents Sea Fisheries Oceanography 9 2 121 135
institution Open Polar
collection Wiley Online Library
op_collection_id crwiley
language English
description Knowledge of the influence of the physical environment on commercially important fish stocks in the North Atlantic has increased during the last decade. To allow this information to be used in fisheries management, some forecast of the environment is important. Predictions of temperature in the Arcto‐boreal Barents Sea have been given for many years, both as subjective opinions of scientists and implicitly in stock assessment assumptions of, e.g., mortality rates. To evaluate an objective statistical forecasting system, we have analysed time series representing mechanisms previously proposed as influencing the temperature of the Barents Sea. These include components of suggested periodic nature, large‐scale advective effects, regional processes, and atmospheric teleconnections. The predictability of Barents Sea temperature based on the above mechanisms was evaluated through calculations of auto‐ and cross‐correlations, linear regression, spectral analysis and autoregressive modelling. Forecasts based on periodic fluctuations in temperature performed poorly. Advection alone did not explain a major part of the variability. The precision of predictions six months ahead varied with season; forecasts from spring to autumn had least uncertainty. A first‐order autoregressive model, including modelled atmospherically driven volume flux to the western Barents Sea during the preceding year and the position of the Gulf Stream off the eastern coast of the USA two years earlier, explained 50% of the total historical temperature variability.
format Article in Journal/Newspaper
author Ottersen, Geir
Ådlandsvik, Bjørn
Loeng, Harald
spellingShingle Ottersen, Geir
Ådlandsvik, Bjørn
Loeng, Harald
Predicting the temperature of the Barents Sea
author_facet Ottersen, Geir
Ådlandsvik, Bjørn
Loeng, Harald
author_sort Ottersen, Geir
title Predicting the temperature of the Barents Sea
title_short Predicting the temperature of the Barents Sea
title_full Predicting the temperature of the Barents Sea
title_fullStr Predicting the temperature of the Barents Sea
title_full_unstemmed Predicting the temperature of the Barents Sea
title_sort predicting the temperature of the barents sea
publisher Wiley
publishDate 2000
url http://dx.doi.org/10.1046/j.1365-2419.2000.00127.x
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1046%2Fj.1365-2419.2000.00127.x
https://onlinelibrary.wiley.com/doi/pdf/10.1046/j.1365-2419.2000.00127.x
geographic Barents Sea
geographic_facet Barents Sea
genre Barents Sea
North Atlantic
genre_facet Barents Sea
North Atlantic
op_source Fisheries Oceanography
volume 9, issue 2, page 121-135
ISSN 1054-6006 1365-2419
op_rights http://onlinelibrary.wiley.com/termsAndConditions#vor
op_doi https://doi.org/10.1046/j.1365-2419.2000.00127.x
container_title Fisheries Oceanography
container_volume 9
container_issue 2
container_start_page 121
op_container_end_page 135
_version_ 1800748732023570432