Do multi‐model ensembles improve reconstruction skill in paleoclimate data assimilation?
Reconstructing past climates remains a difficult task because pre-instrumental observational networks are composed of geographically sparse and noisy paleoclimate proxy records that require statistical techniques to inform complete climate fields. Traditionally, instrumental or climate model statist...
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ftncar:oai:drupal-site.org:articles_24399 2024-04-28T08:37:47+00:00 Do multi‐model ensembles improve reconstruction skill in paleoclimate data assimilation? Parsons, Luke A. (author) Amrhein, Daniel E. (author) Sanchez, Sara C. (author) Tardif, Robert (author) Brennan, M. Kathleen (author) Hakim, Gregory J. (author) 2021-04 https://doi.org/10.1029/2020EA001467 en eng Earth and Space Science--Earth and Space Science--2333-5084--2333-5084 articles:24399 ark:/85065/d7gq725f doi:10.1029/2020EA001467 Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. article Text 2021 ftncar https://doi.org/10.1029/2020EA001467 2024-04-04T17:33:50Z Reconstructing past climates remains a difficult task because pre-instrumental observational networks are composed of geographically sparse and noisy paleoclimate proxy records that require statistical techniques to inform complete climate fields. Traditionally, instrumental or climate model statistical relationships are used to spread information from proxy measurements to other locations and to other climate variables. Here ensembles drawn from single climate models and from combinations of multiple climate models are used to reconstruct temperature variability over the last millennium in idealized experiments. We find that reconstructions derived from multi-model ensembles produce lower error than reconstructions from single-model ensembles when reconstructing independent model and instrumental data. Specifically, we find the largest decreases in error over regions far from proxy locations that are often associated with large uncertainties in model physics, such as mid- and high-latitude ocean and sea-ice regions. Furthermore, we find that multi-model ensemble reconstructions outperform single-model reconstructions that use covariance localization. We propose that multi-model ensembles could be used to improve paleoclimate reconstructions in time periods beyond the last millennium and for climate variables other than air temperature, such as drought metrics or sea ice variables. 1852977 Article in Journal/Newspaper Sea ice OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) Earth and Space Science 8 4 |
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Open Polar |
collection |
OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) |
op_collection_id |
ftncar |
language |
English |
description |
Reconstructing past climates remains a difficult task because pre-instrumental observational networks are composed of geographically sparse and noisy paleoclimate proxy records that require statistical techniques to inform complete climate fields. Traditionally, instrumental or climate model statistical relationships are used to spread information from proxy measurements to other locations and to other climate variables. Here ensembles drawn from single climate models and from combinations of multiple climate models are used to reconstruct temperature variability over the last millennium in idealized experiments. We find that reconstructions derived from multi-model ensembles produce lower error than reconstructions from single-model ensembles when reconstructing independent model and instrumental data. Specifically, we find the largest decreases in error over regions far from proxy locations that are often associated with large uncertainties in model physics, such as mid- and high-latitude ocean and sea-ice regions. Furthermore, we find that multi-model ensemble reconstructions outperform single-model reconstructions that use covariance localization. We propose that multi-model ensembles could be used to improve paleoclimate reconstructions in time periods beyond the last millennium and for climate variables other than air temperature, such as drought metrics or sea ice variables. 1852977 |
author2 |
Parsons, Luke A. (author) Amrhein, Daniel E. (author) Sanchez, Sara C. (author) Tardif, Robert (author) Brennan, M. Kathleen (author) Hakim, Gregory J. (author) |
format |
Article in Journal/Newspaper |
title |
Do multi‐model ensembles improve reconstruction skill in paleoclimate data assimilation? |
spellingShingle |
Do multi‐model ensembles improve reconstruction skill in paleoclimate data assimilation? |
title_short |
Do multi‐model ensembles improve reconstruction skill in paleoclimate data assimilation? |
title_full |
Do multi‐model ensembles improve reconstruction skill in paleoclimate data assimilation? |
title_fullStr |
Do multi‐model ensembles improve reconstruction skill in paleoclimate data assimilation? |
title_full_unstemmed |
Do multi‐model ensembles improve reconstruction skill in paleoclimate data assimilation? |
title_sort |
do multi‐model ensembles improve reconstruction skill in paleoclimate data assimilation? |
publishDate |
2021 |
url |
https://doi.org/10.1029/2020EA001467 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
Earth and Space Science--Earth and Space Science--2333-5084--2333-5084 articles:24399 ark:/85065/d7gq725f doi:10.1029/2020EA001467 |
op_rights |
Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
op_doi |
https://doi.org/10.1029/2020EA001467 |
container_title |
Earth and Space Science |
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8 |
container_issue |
4 |
_version_ |
1797569100923273216 |