SOLA
Online ISSN : 1349-6476
ISSN-L : 1349-6476
Article
Selecting Future Climate Projections of Surface Solar Radiation in Japan
Hideo Shiogama, Rui Ito, Yukiko Imada, Toshiyuki Nakaegawa, Nagio Hirota, Noriko N. Ishizaki, Kiyoshi Takahashi, Izuru Takayabu, Seita Emori
Author information
  • Hideo Shiogama

    National Institute for Environmental Studies
    Atmosphere and Ocean Research Institute, University of Tokyo

  • Rui Ito

    Japan Meteorological Business Support Center
    Meteorological Research Institute, Japan Meteorological Agency

  • Yukiko Imada

    Meteorological Research Institute, Japan Meteorological Agency

  • Toshiyuki Nakaegawa

    Meteorological Research Institute, Japan Meteorological Agency

  • Nagio Hirota

    National Institute for Environmental Studies

  • Noriko N. Ishizaki

    National Institute for Environmental Studies

  • Kiyoshi Takahashi

    National Institute for Environmental Studies

  • Izuru Takayabu

    Meteorological Research Institute, Japan Meteorological Agency

  • Seita Emori

    National Institute for Environmental Studies

Corresponding author

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JOURNAL OPEN ACCESS

2020 Volume 16 Pages 75-79

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  • Published: 2020 Received: December 24, 2019 Released on J-STAGE: May 01, 2020 Accepted: March 16, 2020 Advance online publication: March 30, 2020 Revised: -
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Abstract

The ensemble average projections of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) ensemble show future increases in shortwave radiation at the surface (SW) in Japan. We reveal that the Arctic Oscillation-like atmospheric circulation trends cause cloud cover decreases around Japan, leading to increases in the SW.

In many cases, impact assessment studies use the outputs of only a few models due to limited research resources. We find that the four climate models used in the Japanese multisector impact assessment project, S-8, do not sufficiently capture the uncertainty ranges of the CMIP5 ensemble regarding the SW projections. Therefore, the impact assessments using the SW of these four models can be biased. We develop a novel method to select a better subset of models that are more widely distributed and are not biased, unlike the S-8 models.

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© The Author(s) 2020. This is an open access article published by the Meteorological Society of Japan under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
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