Deliverable No. 2.5 Final report on model developments and their evaluation in coupled mode

In such a remote and harsh environment as the Arctic, the monitoring of essential climate variables is expensive and, therefore, sporadic. In turn, satellites provide observations constrained to the surface. Also, because of technical restrictions, satellites cannot sample, at least not year-round,...

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Main Authors: Ponsoni, Leandro, Gupta, Mukesh, Sterlin, Jean, Massonnet, François, Fichefet, Thierry, Hinrichs, Claudia, Semmler, Tido, Arduini, Gabriele, Ridley, Jeff, Nummelin, Aleksi, Msadek, Rym, Terray, Laurent, Salas y Melia, David, Svensson, Gunilla, Blockley, Ed
Format: Report
Language:English
Published: Zenodo 2021
Subjects:
Online Access:https://dx.doi.org/10.5281/zenodo.4916933
https://zenodo.org/record/4916933
id ftdatacite:10.5281/zenodo.4916933
record_format openpolar
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language English
topic model enhancements
form drag
melt ponds
landfast ice
floe size distribution
multilayer snow scheme
AWI-CM1
HadGEM3-GC3.1
EC-Earth3
ECMWF IFS CY45R1
NEMO3.6-LIM3
GELATO
spellingShingle model enhancements
form drag
melt ponds
landfast ice
floe size distribution
multilayer snow scheme
AWI-CM1
HadGEM3-GC3.1
EC-Earth3
ECMWF IFS CY45R1
NEMO3.6-LIM3
GELATO
Ponsoni, Leandro
Gupta, Mukesh
Sterlin, Jean
Massonnet, François
Fichefet, Thierry
Hinrichs, Claudia
Semmler, Tido
Arduini, Gabriele
Ridley, Jeff
Nummelin, Aleksi
Msadek, Rym
Terray, Laurent
Salas y Melia, David
Svensson, Gunilla
Blockley, Ed
Deliverable No. 2.5 Final report on model developments and their evaluation in coupled mode
topic_facet model enhancements
form drag
melt ponds
landfast ice
floe size distribution
multilayer snow scheme
AWI-CM1
HadGEM3-GC3.1
EC-Earth3
ECMWF IFS CY45R1
NEMO3.6-LIM3
GELATO
description In such a remote and harsh environment as the Arctic, the monitoring of essential climate variables is expensive and, therefore, sporadic. In turn, satellites provide observations constrained to the surface. Also, because of technical restrictions, satellites cannot sample, at least not year-round, a set of essential variables, such as the sea ice thickness. To overcome this difficulty, numerical models are key tools for studying and predicting the Arctic weather and climate. Numerical models aim at reproducing the interactions between different climate components such as the land, atmosphere, ocean, and sea ice. Such interactions are complex and described with non-linear functions. This is one reason numerical models are in constant improvement. The primary goals of Work Package 2 are to promote improvements in numerical models and establish the impact of these improvements on model results, both for the study of the Arctic climate and numerical weather prediction. This deliverable presents a set of model enhancements that are implemented and tested in fully coupled models (AWI-CM1, HadGEM3-GC3.1, EC-Earth3 PRIMAVERA, and ECMWF IFS CY45R1) and forced-mode (NEMO3.6-LIM3 and GELATO). All model developments aim to improve the representation of physical processes that take place in the sea ice or snow. These consist of a better representation of the turbulent exchanges of heat and momentum between the atmosphere and sea ice (form drag), a description of the melt ponds that appear on the sea ice surface during the melt season (melt ponds), a parameterization which takes into account the effect of the sea ice attached to the shore and ocean floor (landfast ice), a scheme which accounts for the size of the floes that form the sea ice cover (floe size distribution), and an improved representation of the snow both over land and sea ice (multilayer snow scheme). We assess the benefit of the model developments based on the following five aspects: (i) changes in various components of the Arctic surface energy budget, (ii) changes in the transfer of momentum from the atmosphere to the ocean, (iii) the overall realism of the simulated climate system, (iv) effects on the Arctic Ocean circulation, and (v) changes in the Arctic climate sensitivity. Common results emerged from the form drag experiments: increased sea ice drift speed in the marginal ice zone, a general decrease in ice thickness, and a marginal decrease of ice concentration at the ice edge in summer. The form drag parameterization improved the large-scale atmospheric and ocean-driven ocean circulation. The melt pond parameterization shows a clear impact on the albedo and sea ice variability and reinforces that a reduced sea ice regime in the Arctic impacts the large-scale, density-driven ocean circulation. The multilayer snow scheme makes the models more sensitive to the surface thermodynamic forcing than the control run with a single layer of snow and shows a more realistic albedo. In numerical weather prediction, the multilayer scheme leads to an improved prediction of the 2-m temperature diurnal cycle over land. Over sea ice, the new scheme reduces large positive biases of outgoing longwave radiation. Fast ice developments lead to more realistic sea ice conditions supported by evidence from observational data. Parameterization of floe size distribution reveals sea ice growth caused by large ice floes in the marginal ice zone during summer.
format Report
author Ponsoni, Leandro
Gupta, Mukesh
Sterlin, Jean
Massonnet, François
Fichefet, Thierry
Hinrichs, Claudia
Semmler, Tido
Arduini, Gabriele
Ridley, Jeff
Nummelin, Aleksi
Msadek, Rym
Terray, Laurent
Salas y Melia, David
Svensson, Gunilla
Blockley, Ed
author_facet Ponsoni, Leandro
Gupta, Mukesh
Sterlin, Jean
Massonnet, François
Fichefet, Thierry
Hinrichs, Claudia
Semmler, Tido
Arduini, Gabriele
Ridley, Jeff
Nummelin, Aleksi
Msadek, Rym
Terray, Laurent
Salas y Melia, David
Svensson, Gunilla
Blockley, Ed
author_sort Ponsoni, Leandro
title Deliverable No. 2.5 Final report on model developments and their evaluation in coupled mode
title_short Deliverable No. 2.5 Final report on model developments and their evaluation in coupled mode
title_full Deliverable No. 2.5 Final report on model developments and their evaluation in coupled mode
title_fullStr Deliverable No. 2.5 Final report on model developments and their evaluation in coupled mode
title_full_unstemmed Deliverable No. 2.5 Final report on model developments and their evaluation in coupled mode
title_sort deliverable no. 2.5 final report on model developments and their evaluation in coupled mode
publisher Zenodo
publishDate 2021
url https://dx.doi.org/10.5281/zenodo.4916933
https://zenodo.org/record/4916933
geographic Arctic
Arctic Ocean
geographic_facet Arctic
Arctic Ocean
genre albedo
Arctic
Arctic Ocean
Sea ice
genre_facet albedo
Arctic
Arctic Ocean
Sea ice
op_relation https://zenodo.org/communities/applicate
https://dx.doi.org/10.5281/zenodo.4916934
https://zenodo.org/communities/applicate
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.4916933
https://doi.org/10.5281/zenodo.4916934
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spelling ftdatacite:10.5281/zenodo.4916933 2023-05-15T13:11:24+02:00 Deliverable No. 2.5 Final report on model developments and their evaluation in coupled mode Ponsoni, Leandro Gupta, Mukesh Sterlin, Jean Massonnet, François Fichefet, Thierry Hinrichs, Claudia Semmler, Tido Arduini, Gabriele Ridley, Jeff Nummelin, Aleksi Msadek, Rym Terray, Laurent Salas y Melia, David Svensson, Gunilla Blockley, Ed 2021 https://dx.doi.org/10.5281/zenodo.4916933 https://zenodo.org/record/4916933 en eng Zenodo https://zenodo.org/communities/applicate https://dx.doi.org/10.5281/zenodo.4916934 https://zenodo.org/communities/applicate 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 model enhancements form drag melt ponds landfast ice floe size distribution multilayer snow scheme AWI-CM1 HadGEM3-GC3.1 EC-Earth3 ECMWF IFS CY45R1 NEMO3.6-LIM3 GELATO Text Report report 2021 ftdatacite https://doi.org/10.5281/zenodo.4916933 https://doi.org/10.5281/zenodo.4916934 2021-11-05T12:55:41Z In such a remote and harsh environment as the Arctic, the monitoring of essential climate variables is expensive and, therefore, sporadic. In turn, satellites provide observations constrained to the surface. Also, because of technical restrictions, satellites cannot sample, at least not year-round, a set of essential variables, such as the sea ice thickness. To overcome this difficulty, numerical models are key tools for studying and predicting the Arctic weather and climate. Numerical models aim at reproducing the interactions between different climate components such as the land, atmosphere, ocean, and sea ice. Such interactions are complex and described with non-linear functions. This is one reason numerical models are in constant improvement. The primary goals of Work Package 2 are to promote improvements in numerical models and establish the impact of these improvements on model results, both for the study of the Arctic climate and numerical weather prediction. This deliverable presents a set of model enhancements that are implemented and tested in fully coupled models (AWI-CM1, HadGEM3-GC3.1, EC-Earth3 PRIMAVERA, and ECMWF IFS CY45R1) and forced-mode (NEMO3.6-LIM3 and GELATO). All model developments aim to improve the representation of physical processes that take place in the sea ice or snow. These consist of a better representation of the turbulent exchanges of heat and momentum between the atmosphere and sea ice (form drag), a description of the melt ponds that appear on the sea ice surface during the melt season (melt ponds), a parameterization which takes into account the effect of the sea ice attached to the shore and ocean floor (landfast ice), a scheme which accounts for the size of the floes that form the sea ice cover (floe size distribution), and an improved representation of the snow both over land and sea ice (multilayer snow scheme). We assess the benefit of the model developments based on the following five aspects: (i) changes in various components of the Arctic surface energy budget, (ii) changes in the transfer of momentum from the atmosphere to the ocean, (iii) the overall realism of the simulated climate system, (iv) effects on the Arctic Ocean circulation, and (v) changes in the Arctic climate sensitivity. Common results emerged from the form drag experiments: increased sea ice drift speed in the marginal ice zone, a general decrease in ice thickness, and a marginal decrease of ice concentration at the ice edge in summer. The form drag parameterization improved the large-scale atmospheric and ocean-driven ocean circulation. The melt pond parameterization shows a clear impact on the albedo and sea ice variability and reinforces that a reduced sea ice regime in the Arctic impacts the large-scale, density-driven ocean circulation. The multilayer snow scheme makes the models more sensitive to the surface thermodynamic forcing than the control run with a single layer of snow and shows a more realistic albedo. In numerical weather prediction, the multilayer scheme leads to an improved prediction of the 2-m temperature diurnal cycle over land. Over sea ice, the new scheme reduces large positive biases of outgoing longwave radiation. Fast ice developments lead to more realistic sea ice conditions supported by evidence from observational data. Parameterization of floe size distribution reveals sea ice growth caused by large ice floes in the marginal ice zone during summer. Report albedo Arctic Arctic Ocean Sea ice DataCite Metadata Store (German National Library of Science and Technology) Arctic Arctic Ocean