Deliverable No. 1.3 Provision of novel metrics, which can be effectively determined from short time series, through ESMValTool
Field campaigns in the Arctic, like the ongoing Year Of Polar Prediction (YOPP, 2017-2019) or the upcoming Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC, 2019-2020) are key opportunities to conduct an evaluation of Earth System Models (ESMs) at the process level. Thi...
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ftdatacite:10.5281/zenodo.3569273 2023-05-15T14:50:54+02:00 Deliverable No. 1.3 Provision of novel metrics, which can be effectively determined from short time series, through ESMValTool Massonnet, François 2018 https://dx.doi.org/10.5281/zenodo.3569273 https://zenodo.org/record/3569273 en eng Zenodo https://dx.doi.org/10.5281/zenodo.3569274 Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY Text Project deliverable article-journal ScholarlyArticle 2018 ftdatacite https://doi.org/10.5281/zenodo.3569273 https://doi.org/10.5281/zenodo.3569274 2021-11-05T12:55:41Z Field campaigns in the Arctic, like the ongoing Year Of Polar Prediction (YOPP, 2017-2019) or the upcoming Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC, 2019-2020) are key opportunities to conduct an evaluation of Earth System Models (ESMs) at the process level. This raises, however, a number of challenges as ESMs are not necessarily in phase with the actual climate for periods as short as one or two years. Here, a metric is developed to evaluate ESMs on their ability to simulate the snow and ice thicknesses and the underlying process of vertical heat conduction. The metric is derived from a diagnostic called the “heat conductivity index” that has the appealing property to be stable over time, and hence suitable for evaluation of Arctic sea ice where trends are generally strong and interannual variability high. The metric has been incorporated to the ESMValTool, a reference package for model evaluation. This will ensure wider use by the APPLICATE partners (WP1, 2, 4) but also by researchers analyzing the Coupled Model Intercomparison Project, phase 6 (CMIP6) dataset. Text Arctic Sea ice DataCite Metadata Store (German National Library of Science and Technology) Arctic |
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DataCite Metadata Store (German National Library of Science and Technology) |
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English |
description |
Field campaigns in the Arctic, like the ongoing Year Of Polar Prediction (YOPP, 2017-2019) or the upcoming Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC, 2019-2020) are key opportunities to conduct an evaluation of Earth System Models (ESMs) at the process level. This raises, however, a number of challenges as ESMs are not necessarily in phase with the actual climate for periods as short as one or two years. Here, a metric is developed to evaluate ESMs on their ability to simulate the snow and ice thicknesses and the underlying process of vertical heat conduction. The metric is derived from a diagnostic called the “heat conductivity index” that has the appealing property to be stable over time, and hence suitable for evaluation of Arctic sea ice where trends are generally strong and interannual variability high. The metric has been incorporated to the ESMValTool, a reference package for model evaluation. This will ensure wider use by the APPLICATE partners (WP1, 2, 4) but also by researchers analyzing the Coupled Model Intercomparison Project, phase 6 (CMIP6) dataset. |
format |
Text |
author |
Massonnet, François |
spellingShingle |
Massonnet, François Deliverable No. 1.3 Provision of novel metrics, which can be effectively determined from short time series, through ESMValTool |
author_facet |
Massonnet, François |
author_sort |
Massonnet, François |
title |
Deliverable No. 1.3 Provision of novel metrics, which can be effectively determined from short time series, through ESMValTool |
title_short |
Deliverable No. 1.3 Provision of novel metrics, which can be effectively determined from short time series, through ESMValTool |
title_full |
Deliverable No. 1.3 Provision of novel metrics, which can be effectively determined from short time series, through ESMValTool |
title_fullStr |
Deliverable No. 1.3 Provision of novel metrics, which can be effectively determined from short time series, through ESMValTool |
title_full_unstemmed |
Deliverable No. 1.3 Provision of novel metrics, which can be effectively determined from short time series, through ESMValTool |
title_sort |
deliverable no. 1.3 provision of novel metrics, which can be effectively determined from short time series, through esmvaltool |
publisher |
Zenodo |
publishDate |
2018 |
url |
https://dx.doi.org/10.5281/zenodo.3569273 https://zenodo.org/record/3569273 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_relation |
https://dx.doi.org/10.5281/zenodo.3569274 |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.3569273 https://doi.org/10.5281/zenodo.3569274 |
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1766321954582691840 |