Towards operational predictions of the near-term climate View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-02

AUTHORS

Yochanan Kushnir, Adam A. Scaife, Raymond Arritt, Gianpaolo Balsamo, George Boer, Francisco Doblas-Reyes, Ed Hawkins, Masahide Kimoto, Rupa Kumar Kolli, Arun Kumar, Daniela Matei, Katja Matthes, Wolfgang A. Müller, Terence O’Kane, Judith Perlwitz, Scott Power, Marilyn Raphael, Akihiko Shimpo, Doug Smith, Matthias Tuma, Bo Wu

ABSTRACT

Near-term climate predictions — which operate on annual to decadal timescales — offer benefits for climate adaptation and resilience, and are thus important for society. Although skilful near-term predictions are now possible, particularly when coupled models are initialized from the current climate state (most importantly from the ocean), several scientific challenges remain, including gaps in understanding and modelling the underlying physical mechanisms. This Perspective discusses how these challenges can be overcome, outlining concrete steps towards the provision of operational near-term climate predictions. Progress in this endeavour will bridge the gap between current seasonal forecasts and century-scale climate change projections, allowing a seamless climate service delivery chain to be established. Near-term climate predictions bridge the gap between seasonal forecasts and long-term projections. This Perspective outlines the challenges and opportunities for near-term climate prediction, highlighting the need for co-ordinated efforts to benefit society. More... »

PAGES

94-101

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    URI

    http://scigraph.springernature.com/pub.10.1038/s41558-018-0359-7

    DOI

    http://dx.doi.org/10.1038/s41558-018-0359-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1111375494


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    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

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    N-Triples is a line-based linked data format ideal for batch operations.

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    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41558-018-0359-7'


     

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