2017
DOI: 10.1002/2016gl072342
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Drivers of Arctic Ocean warming in CMIP5 models

Abstract: We investigate changes in the Arctic Ocean energy budget simulated by 26 general circulation models from the Coupled Model Intercomparison Project Phase 5 framework. Our goal is to understand whether the Arctic Ocean warming between 1961 and 2099 is primarily driven by changes in the net atmospheric surface flux or by changes in the meridional oceanic heat flux. We find that the simulated Arctic Ocean warming is driven by positive anomalies in the net atmospheric surface flux in 11 models, by positive anomalie… Show more

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Cited by 33 publications
(38 citation statements)
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“…This ocean warming largely explains the observed Barents Sea winter ice variability (Årthun et al 2012(Årthun et al , Smedsrud et al 2013, and provides a useful predictor for the annual mean sea-ice cover in the Barents Sea (Onarheim et al 2015). The Barents Sea region has also been identified as key for explaining model differences between oceanic and atmospheric pathways of energy transfer to the central Arctic Ocean (Burgard and Notz 2017). Given this relationship, others have gone further to suggest that some recovery of the sea-ice cover may be possible if the spin-down of the thermohaline circulation continues (Yeager et al 2015).…”
Section: Oceanic Pathwaysmentioning
confidence: 93%
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“…This ocean warming largely explains the observed Barents Sea winter ice variability (Årthun et al 2012(Årthun et al , Smedsrud et al 2013, and provides a useful predictor for the annual mean sea-ice cover in the Barents Sea (Onarheim et al 2015). The Barents Sea region has also been identified as key for explaining model differences between oceanic and atmospheric pathways of energy transfer to the central Arctic Ocean (Burgard and Notz 2017). Given this relationship, others have gone further to suggest that some recovery of the sea-ice cover may be possible if the spin-down of the thermohaline circulation continues (Yeager et al 2015).…”
Section: Oceanic Pathwaysmentioning
confidence: 93%
“…Having thus established that a combination of internal variability and anthropogenic forcing is largely responsible for the observed ice loss, the question naturally arises how specifically these drivers affect the Arctic sea ice cover. A study by Burgard and Notz (2017) has found that CMIP5 models disagree on whether the anomalous heating of the Arctic Ocean, and thus the loss of Arctic sea ice, primarily occurs through changes in vertical heat exchanges with the atmosphere (as is the case in 11 CMIP5 models), primarily through changes in meridional ocean heat flux (as is the case in 11 other CMIP5 models) or through a combination of both (as is the case in 4 CMIP5 models). This suggests that our understanding of how precisely the heat for the observed sea ice melt is provided to the sea ice is still surprisingly limited.…”
Section: Atmospheric Pathwaysmentioning
confidence: 99%
“…In recent years the surface warming has been the largest in the Barents Sea, where the position of the sea ice edge has been linked to variations in the inflow of Atlantic Water (AW; Årthun et al, 2012;Koenigk & Brodeau, 2017;Onarheim & Årthun, 2017;Onarheim et al, 2015Onarheim et al, , 2018Sandø et al, 2014). For the future decades and century, it is still an open research question to which extent the Arctic Ocean will be dominated by enhanced warming through the surface or by an increased poleward ocean heat transport (Burgard & Notz, 2017). Here we investigate how ocean heat transport toward the Arctic Ocean has varied in the 20th century and what the controlling mechanisms are.…”
Section: Introductionmentioning
confidence: 99%
“…While several multimodel studies based on Coupled Model Intercomparison Project Phase 5 (CMIP5) models have focused on the Arctic (Barnes & Polvani, 2015;Clara & Dirk, 2017;Franzke et al, 2017;Huang et al, 2017), none have, to our knowledge, looked at different drivers independently. The Precipitation Driver Response Model Intercomparison Project (PDRMIP) provides a unique data set that allows for investigations into climate responses to separate and clearly defined climate drivers, such as greenhouse gases or aerosols, in a multimodel framework.…”
Section: Introductionmentioning
confidence: 99%