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dc.contributor.author
Merryfield, William
dc.contributor.author
Doblas Reyes, Francisco
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Ferranti, Laura
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Jeong, Jee-Hoon
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Orsolini, Yvan
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Saurral, Ramiro Ignacio
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Scaife, Adam
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Tolstykh, Mikhail
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Rixen, Michel
dc.date.available
2018-09-19T19:34:08Z
dc.date.issued
2017-11
dc.identifier.citation
Merryfield, William; Doblas Reyes, Francisco; Ferranti, Laura; Jeong, Jee-Hoon; Orsolini, Yvan; et al.; Advancing Climate Forecasting; American Geophysical Union; Eos; 11-2017; 1-7
dc.identifier.issn
0096-3941
dc.identifier.uri
http://hdl.handle.net/11336/60306
dc.description.abstract
Climate forecasts predict weather averages and other climatic properties from a few weeks to a few years in advance. Increasingly, forecasters are using comprehensive models of Earth?s climate system to make such predictions. Researchers also use climate models to project forced changes many decades into the future under assumed scenarios for human influence. Those simulations typically start in preindustrial times, so far in the past that details of their initial states have little influence in the present era. By contrast, climate forecasts begin from more recent observed climate system states, much like weather forecasts. For this reason, they are sometimes referred to as ?initialized climate predictions.? Climate forecasts are produced at numerous operational [Graham et al., 2011] and research centers worldwide. Models and approaches vary, and by coordinating research efforts, the modeling community can make even greater progress. The Working Group on Subseasonal to Interdecadal Prediction (WGSIP) of the World Climate Research Programme (WCRP) facilitates such coordination through a program of numerical experimentation?evaluating model responses to different inputs?aimed at assessing and improving climate forecasts. WGSIP currently supports a project that archives hindcasts; this is a major community resource for climate forecasting research. It also supports three additional targeted research projects aimed at advancing specific aspects of climate forecasting. These projects examine how well climate forecast models represent global influences of tropical rainfall, assess how snow predictably influences climate, and study how model drifts and biases develop and affect climate forecasts.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
American Geophysical Union
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Climate Forecasts
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Seasonal
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Chfp
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Decadal
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Meteorología y Ciencias Atmosféricas
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Ciencias de la Tierra y relacionadas con el Medio Ambiente
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CIENCIAS NATURALES Y EXACTAS
dc.title
Advancing Climate Forecasting
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-09-07T13:51:55Z
dc.journal.pagination
1-7
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Merryfield, William. Canadian Centre for Climate Modelling and Analysis; Canadá
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Fil: Doblas Reyes, Francisco. Barcelona Supercomputing Center; España
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Fil: Ferranti, Laura. European Centre for Medium-Range Weather Forecasts; Reino Unido
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Fil: Jeong, Jee-Hoon. Chonnam National University; Corea del Sur
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Fil: Orsolini, Yvan. Norwegian Institute for Air Research; Noruega
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Fil: Saurral, Ramiro Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina
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Fil: Scaife, Adam. Met Office Hadley Centre; Reino Unido
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Fil: Tolstykh, Mikhail. Russian Academy of Sciences. Institute of Numerical Mathematics; Argentina
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Fil: Rixen, Michel. World Meteorological Organization; Suiza
dc.journal.title
Eos
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://eos.org/project-updates/advancing-climate-forecasting
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1029/2017EO086891
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