Arctic Warming Revealed by Multiple CMIP6 Models: Evaluation of Historical Simulations and Quantification of Future Projection Uncertainties
(2021) In Journal of Climate 34(12). p.4871-4892- Abstract
- The Arctic has experienced a warming rate higher than the global mean in the past decades, but previous studies show that there are large uncertainties associated with future Arctic temperature projections. In this study, near-surface mean temperatures in the Arctic are analyzed from 22 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Compared with the ERA5 reanalysis, most CMIP6 models underestimate the observed mean temperature in the Arctic during 1979–2014. The largest cold biases are found over the Greenland Sea the Barents Sea, and the Kara Sea. Under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, the multimodel ensemble mean of 22 CMIP6 models exhibits significant Arctic warming in the future... (More)
- The Arctic has experienced a warming rate higher than the global mean in the past decades, but previous studies show that there are large uncertainties associated with future Arctic temperature projections. In this study, near-surface mean temperatures in the Arctic are analyzed from 22 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Compared with the ERA5 reanalysis, most CMIP6 models underestimate the observed mean temperature in the Arctic during 1979–2014. The largest cold biases are found over the Greenland Sea the Barents Sea, and the Kara Sea. Under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, the multimodel ensemble mean of 22 CMIP6 models exhibits significant Arctic warming in the future and the warming rate is more than twice that of the global/Northern Hemisphere mean. Model spread is the largest contributor to the overall uncertainty in projections, which accounts for 55.4% of the total uncertainty at the start of projections in 2015 and remains at 32.9% at the end of projections in 2095. Internal variability uncertainty accounts for 39.3% of the total uncertainty at the start of projections but decreases to 6.5% at the end of the twenty-first century, while scenario uncertainty rapidly increases from 5.3% to 60.7% over the period from 2015 to 2095. It is found that the largest model uncertainties are consistent cold bias in the oceanic regions in the models, which is connected with excessive sea ice area caused by the weak Atlantic poleward heat transport. These results suggest that large intermodel spread and uncertainties exist in the CMIP6 models’ simulation and projection of the Arctic near-surface temperature and that there are different responses over the ocean and land in the Arctic to greenhouse gas forcing. Future research needs to pay more attention to the different characteristics and mechanisms of Arctic Ocean and land warming to reduce the spread. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/c9b31d42-8c7c-486f-93e9-aa664147447e
- author
- Cai, Ziyi ; You, Qinglong ; Wu, Fangying ; Chen, Hans W. LU ; Chen, Deliang and Cohen, Judah
- organization
- publishing date
- 2021-06-01
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Climate
- volume
- 34
- issue
- 12
- pages
- 22 pages
- publisher
- American Meteorological Society
- external identifiers
-
- scopus:85106962521
- ISSN
- 0894-8755
- DOI
- 10.1175/JCLI-D-20-0791.1
- language
- English
- LU publication?
- yes
- id
- c9b31d42-8c7c-486f-93e9-aa664147447e
- date added to LUP
- 2021-06-01 14:54:43
- date last changed
- 2023-02-21 11:38:19
@article{c9b31d42-8c7c-486f-93e9-aa664147447e,
abstract = {{The Arctic has experienced a warming rate higher than the global mean in the past decades, but previous studies show that there are large uncertainties associated with future Arctic temperature projections. In this study, near-surface mean temperatures in the Arctic are analyzed from 22 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Compared with the ERA5 reanalysis, most CMIP6 models underestimate the observed mean temperature in the Arctic during 1979–2014. The largest cold biases are found over the Greenland Sea the Barents Sea, and the Kara Sea. Under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, the multimodel ensemble mean of 22 CMIP6 models exhibits significant Arctic warming in the future and the warming rate is more than twice that of the global/Northern Hemisphere mean. Model spread is the largest contributor to the overall uncertainty in projections, which accounts for 55.4% of the total uncertainty at the start of projections in 2015 and remains at 32.9% at the end of projections in 2095. Internal variability uncertainty accounts for 39.3% of the total uncertainty at the start of projections but decreases to 6.5% at the end of the twenty-first century, while scenario uncertainty rapidly increases from 5.3% to 60.7% over the period from 2015 to 2095. It is found that the largest model uncertainties are consistent cold bias in the oceanic regions in the models, which is connected with excessive sea ice area caused by the weak Atlantic poleward heat transport. These results suggest that large intermodel spread and uncertainties exist in the CMIP6 models’ simulation and projection of the Arctic near-surface temperature and that there are different responses over the ocean and land in the Arctic to greenhouse gas forcing. Future research needs to pay more attention to the different characteristics and mechanisms of Arctic Ocean and land warming to reduce the spread.}},
author = {{Cai, Ziyi and You, Qinglong and Wu, Fangying and Chen, Hans W. and Chen, Deliang and Cohen, Judah}},
issn = {{0894-8755}},
language = {{eng}},
month = {{06}},
number = {{12}},
pages = {{4871--4892}},
publisher = {{American Meteorological Society}},
series = {{Journal of Climate}},
title = {{Arctic Warming Revealed by Multiple CMIP6 Models: Evaluation of Historical Simulations and Quantification of Future Projection Uncertainties}},
url = {{http://dx.doi.org/10.1175/JCLI-D-20-0791.1}},
doi = {{10.1175/JCLI-D-20-0791.1}},
volume = {{34}},
year = {{2021}},
}