Identification of the surface state influence in representing the Arctic warming by coordinated atmosphere-only simulations (D3.1)
Summary: Arctic amplification metrics: The surface air temperature (SAT) anomalies, trends and variability have been used to quantify the Arctic amplification (AA). The use of different metrics, as well as the choice of dataset can affect conclusions about the magnitude and temporal variability of A...
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ftzenodo:oai:zenodo.org:3559462 2024-09-15T18:08:07+00:00 Identification of the surface state influence in representing the Arctic warming by coordinated atmosphere-only simulations (D3.1) Gao, Yongqi Davy, Richard Suo, Lingling Gastineau, Guillaume Kwon, Young-Oh Semenov, Vladimir Liu , Yang 2019-09-30 https://doi.org/10.5281/zenodo.3559462 unknown Zenodo https://zenodo.org/communities/eu https://zenodo.org/communities/blue-actionh2020 https://doi.org/10.5281/zenodo.3559461 https://doi.org/10.5281/zenodo.3559462 oai:zenodo.org:3559462 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode info:eu-repo/semantics/report 2019 ftzenodo https://doi.org/10.5281/zenodo.355946210.5281/zenodo.3559461 2024-07-26T13:02:54Z Summary: Arctic amplification metrics: The surface air temperature (SAT) anomalies, trends and variability have been used to quantify the Arctic amplification (AA). The use of different metrics, as well as the choice of dataset can affect conclusions about the magnitude and temporal variability of AA. We reviewed the established metrics of AA to see how well they agree upon the temporal signature of AA and assess the consistency in these metrics across commonly-used datasets which cover both the early and late 20th century warming in the Arctic. We find the NOAA 20th Century Reanalysis most closely matches the observations when using metrics based upon the trends, variability and the amplification of SAT anomalies in the Arctic, and the ERA 20th Century Reanalysis is closest to the observations in the SAT anomalies and variability of SAT anomalies. The largest differences between the century-long reanalysis products and observations are during the early warming period. In the modern warming period, the high density of observations strongly constrains all the reanalysis products, whether they include satellite observations or only surface observations. Thus, all the reanalysis and observation products produce very similar magnitudes and temporal variability in the degree of AA during the recent warming period (Davy et al., 2018). Sea ice free Arctic contributes to the projected warming minimum in North Atlantic: Projected global warming is not spatially uniform and one of the minima in warming occurs in the North Atlantic (NA). Several models from the Coupled Model Intercomparison Project Phase 5 even projected a slight NA cooling in 2081–2100 relative to 1986–2005. Here we show that, by coupled model simulations, an autumn (September to November) sea-ice free Arctic contributes to the NA warming minimum by weakening the Atlantic meridional overturning circulation (Suo et al., 2017). Design and finalize the set-up for the atmosphere-only model coordinated experiments: Four multi-model atmospheric coordinated ... Report Global warming North Atlantic Sea ice Zenodo |
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Summary: Arctic amplification metrics: The surface air temperature (SAT) anomalies, trends and variability have been used to quantify the Arctic amplification (AA). The use of different metrics, as well as the choice of dataset can affect conclusions about the magnitude and temporal variability of AA. We reviewed the established metrics of AA to see how well they agree upon the temporal signature of AA and assess the consistency in these metrics across commonly-used datasets which cover both the early and late 20th century warming in the Arctic. We find the NOAA 20th Century Reanalysis most closely matches the observations when using metrics based upon the trends, variability and the amplification of SAT anomalies in the Arctic, and the ERA 20th Century Reanalysis is closest to the observations in the SAT anomalies and variability of SAT anomalies. The largest differences between the century-long reanalysis products and observations are during the early warming period. In the modern warming period, the high density of observations strongly constrains all the reanalysis products, whether they include satellite observations or only surface observations. Thus, all the reanalysis and observation products produce very similar magnitudes and temporal variability in the degree of AA during the recent warming period (Davy et al., 2018). Sea ice free Arctic contributes to the projected warming minimum in North Atlantic: Projected global warming is not spatially uniform and one of the minima in warming occurs in the North Atlantic (NA). Several models from the Coupled Model Intercomparison Project Phase 5 even projected a slight NA cooling in 2081–2100 relative to 1986–2005. Here we show that, by coupled model simulations, an autumn (September to November) sea-ice free Arctic contributes to the NA warming minimum by weakening the Atlantic meridional overturning circulation (Suo et al., 2017). Design and finalize the set-up for the atmosphere-only model coordinated experiments: Four multi-model atmospheric coordinated ... |
format |
Report |
author |
Gao, Yongqi Davy, Richard Suo, Lingling Gastineau, Guillaume Kwon, Young-Oh Semenov, Vladimir Liu , Yang |
spellingShingle |
Gao, Yongqi Davy, Richard Suo, Lingling Gastineau, Guillaume Kwon, Young-Oh Semenov, Vladimir Liu , Yang Identification of the surface state influence in representing the Arctic warming by coordinated atmosphere-only simulations (D3.1) |
author_facet |
Gao, Yongqi Davy, Richard Suo, Lingling Gastineau, Guillaume Kwon, Young-Oh Semenov, Vladimir Liu , Yang |
author_sort |
Gao, Yongqi |
title |
Identification of the surface state influence in representing the Arctic warming by coordinated atmosphere-only simulations (D3.1) |
title_short |
Identification of the surface state influence in representing the Arctic warming by coordinated atmosphere-only simulations (D3.1) |
title_full |
Identification of the surface state influence in representing the Arctic warming by coordinated atmosphere-only simulations (D3.1) |
title_fullStr |
Identification of the surface state influence in representing the Arctic warming by coordinated atmosphere-only simulations (D3.1) |
title_full_unstemmed |
Identification of the surface state influence in representing the Arctic warming by coordinated atmosphere-only simulations (D3.1) |
title_sort |
identification of the surface state influence in representing the arctic warming by coordinated atmosphere-only simulations (d3.1) |
publisher |
Zenodo |
publishDate |
2019 |
url |
https://doi.org/10.5281/zenodo.3559462 |
genre |
Global warming North Atlantic Sea ice |
genre_facet |
Global warming North Atlantic Sea ice |
op_relation |
https://zenodo.org/communities/eu https://zenodo.org/communities/blue-actionh2020 https://doi.org/10.5281/zenodo.3559461 https://doi.org/10.5281/zenodo.3559462 oai:zenodo.org:3559462 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.355946210.5281/zenodo.3559461 |
_version_ |
1810445458571526144 |