An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000 - 1849
We present 50-member ensemble reconstruction of global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000 – 1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resoluti...
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ftdatacite:10.6084/m9.figshare.c.5369309 2023-05-15T18:17:46+02:00 An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000 - 1849 Brönnimann, Stefan Kennedy, John Rayner, Nick A Samakinwa, Eric Valler, Veronika Hand, Ralf Neukom, Raphael Gómez-Navarro, Juan José 2021 https://dx.doi.org/10.6084/m9.figshare.c.5369309 https://springernature.figshare.com/collections/An_ensemble_reconstruction_of_global_monthly_sea_surface_temperature_and_sea_ice_concentration_1000_-_1849/5369309 unknown figshare https://dx.doi.org/10.1038/s41597-021-01043-1 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Oceanography FOS Earth and related environmental sciences 40605 Palaeoclimatology Climate Science Collection article 2021 ftdatacite https://doi.org/10.6084/m9.figshare.c.5369309 https://doi.org/10.1038/s41597-021-01043-1 2021-11-05T12:55:41Z We present 50-member ensemble reconstruction of global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000 – 1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resolution annual temperature ensemble reconstructions, which are then augmented with intra-annual and sub-grid scale variability. For this, the intra-annual component of HadISST.2.0 and oceanic indices estimated from the reconstructed annual mean are used to develop grid-based multiple linear regressions with partitioned variance, in a monthly stratified approach. Similarly, we reconstruct SIC using analog resampling of HadISST.2.0 SIC (1941 – 2000), for both hemispheres. Analogs are pooled in four seasons, comprising of 3-months each. The best analogs are selected based on the correlation between each member of the reconstructed SST and its target. For the period 1780 to 1849, we assimilate historical observations of SST and night-time marine air temperature from the ICOADS dataset into our reconstruction using an offline Ensemble Kalman Filter approach. Article in Journal/Newspaper Sea ice DataCite Metadata Store (German National Library of Science and Technology) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Oceanography FOS Earth and related environmental sciences 40605 Palaeoclimatology Climate Science |
spellingShingle |
Oceanography FOS Earth and related environmental sciences 40605 Palaeoclimatology Climate Science Brönnimann, Stefan Kennedy, John Rayner, Nick A Samakinwa, Eric Valler, Veronika Hand, Ralf Neukom, Raphael Gómez-Navarro, Juan José An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000 - 1849 |
topic_facet |
Oceanography FOS Earth and related environmental sciences 40605 Palaeoclimatology Climate Science |
description |
We present 50-member ensemble reconstruction of global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000 – 1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resolution annual temperature ensemble reconstructions, which are then augmented with intra-annual and sub-grid scale variability. For this, the intra-annual component of HadISST.2.0 and oceanic indices estimated from the reconstructed annual mean are used to develop grid-based multiple linear regressions with partitioned variance, in a monthly stratified approach. Similarly, we reconstruct SIC using analog resampling of HadISST.2.0 SIC (1941 – 2000), for both hemispheres. Analogs are pooled in four seasons, comprising of 3-months each. The best analogs are selected based on the correlation between each member of the reconstructed SST and its target. For the period 1780 to 1849, we assimilate historical observations of SST and night-time marine air temperature from the ICOADS dataset into our reconstruction using an offline Ensemble Kalman Filter approach. |
format |
Article in Journal/Newspaper |
author |
Brönnimann, Stefan Kennedy, John Rayner, Nick A Samakinwa, Eric Valler, Veronika Hand, Ralf Neukom, Raphael Gómez-Navarro, Juan José |
author_facet |
Brönnimann, Stefan Kennedy, John Rayner, Nick A Samakinwa, Eric Valler, Veronika Hand, Ralf Neukom, Raphael Gómez-Navarro, Juan José |
author_sort |
Brönnimann, Stefan |
title |
An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000 - 1849 |
title_short |
An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000 - 1849 |
title_full |
An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000 - 1849 |
title_fullStr |
An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000 - 1849 |
title_full_unstemmed |
An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000 - 1849 |
title_sort |
ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000 - 1849 |
publisher |
figshare |
publishDate |
2021 |
url |
https://dx.doi.org/10.6084/m9.figshare.c.5369309 https://springernature.figshare.com/collections/An_ensemble_reconstruction_of_global_monthly_sea_surface_temperature_and_sea_ice_concentration_1000_-_1849/5369309 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://dx.doi.org/10.1038/s41597-021-01043-1 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.c.5369309 https://doi.org/10.1038/s41597-021-01043-1 |
_version_ |
1766192948478738432 |