An anatomy of the forecast errors in a seasonal prediction system with EC-Earth

Initialization is a key step when performing climate predictions, and for this the use of the latest high-quality observations and their assimilation in the model realm is of paramount importance. Less attention has been paid to other essential aspects of initialization that are equally important. F...

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Main Authors: Cruz-García, Rubén, Ortega, Pablo, Acosta Navarro, Juan C., Massonnet, François, Doblas-Reyes, Francisco J.
Format: Conference Object
Language:unknown
Published: Zenodo 2019
Subjects:
Online Access:https://doi.org/10.5281/zenodo.3567552
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spelling ftzenodo:oai:zenodo.org:3567552 2024-09-15T18:10:11+00:00 An anatomy of the forecast errors in a seasonal prediction system with EC-Earth Cruz-García, Rubén Ortega, Pablo Acosta Navarro, Juan C. Massonnet, François Doblas-Reyes, Francisco J. 2019-03-26 https://doi.org/10.5281/zenodo.3567552 unknown Zenodo https://zenodo.org/communities/applicate https://zenodo.org/communities/eu https://doi.org/10.5281/zenodo.3567551 https://doi.org/10.5281/zenodo.3567552 oai:zenodo.org:3567552 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Arctic Sea ice Forecast Shock Initialization Bias info:eu-repo/semantics/conferencePoster 2019 ftzenodo https://doi.org/10.5281/zenodo.356755210.5281/zenodo.3567551 2024-07-27T05:17:29Z Initialization is a key step when performing climate predictions, and for this the use of the latest high-quality observations and their assimilation in the model realm is of paramount importance. Less attention has been paid to other essential aspects of initialization that are equally important. For example, inconsistencies between the initial conditions (ICs) used for the different model components can cause important initialization shocks, hindering the prediction capacity during the first weeks of the forecast. In this study we investigate this and other different contributions to the forecast error in a seasonal prediction system with the EC-Earth general circulation model where sea ice is initialized via Ensemble Kalman filter assimilation of European Space Agency (ESA) derived sea ice concentrations. Large initial forecast errors in Arctic sea ice appear in regions of high observational uncertainty and little model spread, a combination that brings the assimilation, and in turn the ICs, close to the model attractor and far from the observations. We also investigated the development of the model drift during the first forecast month, and how it competes with the initial shock due to the inconsistency in ICs. After 24 (19) days the drift, as characterized by the systematic model error, becomes the largest contributor to the forecast error for the May (November) initialized forecasts, while the initial inconsistency dominates in the previous days. However, there are regions like the Greenland Sea for which the impact of the ICs inconsistency is still present after one month. Moreover, the development of both types of errors is sensitive to the month of initialization: the shock is more pronounced in November than in May. The main differences between both months relate to the systematic error, which is much higher in November, as well as to the direction of the shock with respect to the seasonal trend. In both cases the shock leads to sea ice melting, but, unlike in May, in November it happens in a context ... Conference Object Greenland Greenland Sea Sea ice Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language unknown
topic Arctic
Sea ice
Forecast
Shock
Initialization
Bias
spellingShingle Arctic
Sea ice
Forecast
Shock
Initialization
Bias
Cruz-García, Rubén
Ortega, Pablo
Acosta Navarro, Juan C.
Massonnet, François
Doblas-Reyes, Francisco J.
An anatomy of the forecast errors in a seasonal prediction system with EC-Earth
topic_facet Arctic
Sea ice
Forecast
Shock
Initialization
Bias
description Initialization is a key step when performing climate predictions, and for this the use of the latest high-quality observations and their assimilation in the model realm is of paramount importance. Less attention has been paid to other essential aspects of initialization that are equally important. For example, inconsistencies between the initial conditions (ICs) used for the different model components can cause important initialization shocks, hindering the prediction capacity during the first weeks of the forecast. In this study we investigate this and other different contributions to the forecast error in a seasonal prediction system with the EC-Earth general circulation model where sea ice is initialized via Ensemble Kalman filter assimilation of European Space Agency (ESA) derived sea ice concentrations. Large initial forecast errors in Arctic sea ice appear in regions of high observational uncertainty and little model spread, a combination that brings the assimilation, and in turn the ICs, close to the model attractor and far from the observations. We also investigated the development of the model drift during the first forecast month, and how it competes with the initial shock due to the inconsistency in ICs. After 24 (19) days the drift, as characterized by the systematic model error, becomes the largest contributor to the forecast error for the May (November) initialized forecasts, while the initial inconsistency dominates in the previous days. However, there are regions like the Greenland Sea for which the impact of the ICs inconsistency is still present after one month. Moreover, the development of both types of errors is sensitive to the month of initialization: the shock is more pronounced in November than in May. The main differences between both months relate to the systematic error, which is much higher in November, as well as to the direction of the shock with respect to the seasonal trend. In both cases the shock leads to sea ice melting, but, unlike in May, in November it happens in a context ...
format Conference Object
author Cruz-García, Rubén
Ortega, Pablo
Acosta Navarro, Juan C.
Massonnet, François
Doblas-Reyes, Francisco J.
author_facet Cruz-García, Rubén
Ortega, Pablo
Acosta Navarro, Juan C.
Massonnet, François
Doblas-Reyes, Francisco J.
author_sort Cruz-García, Rubén
title An anatomy of the forecast errors in a seasonal prediction system with EC-Earth
title_short An anatomy of the forecast errors in a seasonal prediction system with EC-Earth
title_full An anatomy of the forecast errors in a seasonal prediction system with EC-Earth
title_fullStr An anatomy of the forecast errors in a seasonal prediction system with EC-Earth
title_full_unstemmed An anatomy of the forecast errors in a seasonal prediction system with EC-Earth
title_sort anatomy of the forecast errors in a seasonal prediction system with ec-earth
publisher Zenodo
publishDate 2019
url https://doi.org/10.5281/zenodo.3567552
genre Greenland
Greenland Sea
Sea ice
genre_facet Greenland
Greenland Sea
Sea ice
op_relation https://zenodo.org/communities/applicate
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3567551
https://doi.org/10.5281/zenodo.3567552
oai:zenodo.org:3567552
op_rights info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
op_doi https://doi.org/10.5281/zenodo.356755210.5281/zenodo.3567551
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