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Title: Attributing Historical Changes in Probabilities of Record-Breaking Daily Temperature and Precipitation Extreme Events

Abstract

Here, we describe two unprecedented large (100-member), longterm (61-year) ensembles based on MRI-AGCM3.2, which were driven by historical and non-warming climate forcing. These ensembles comprise the "Database for Policy Decision making for Future climate change (d4PDF)". We compare these ensembles to large ensembles based on another climate model, as well as to observed data, to investigate the influence of anthropogenic activities on historical changes in the numbers of record-breaking events, including: the annual coldest daily minimum temperature (TNn), the annual warmest daily maximum temperature (TXx) and the annual most intense daily precipitation event (Rx1day). These two climate model ensembles indicate that human activity has already had statistically significant impacts on the number of record-breaking extreme events worldwide mainly in the Northern Hemisphere land. Specifically, human activities have altered the likelihood that a wider area globally would suffer record-breaking TNn, TXx and Rx1day events than that observed over the 2001- 2010 period by a factor of at least 0.6, 5.4 and 1.3, respectively. However, we also find that the estimated spatial patterns and amplitudes of anthropogenic impacts on the probabilities of record-breaking events are sensitive to the climate model and/or natural-world boundary conditions used in the attribution studies.

Authors:
 [1];  [2];  [3];  [2];  [4];  [2];  [5];  [6];  [7];  [7];  [2];  [7];  [7]
  1. National Inst. for Environmental Studies, Tsukuba (Japan). Center for Global Environmental Research
  2. Meteorological Research Inst., Tsukuba (Japan)
  3. Univ. of Tokyo (Japan). Research Center for Advanced Science and Technology
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Division
  5. Univ. of Tsukuba (Japan). Faculty of Life and Environmental Sciences
  6. Japan Agency for Marine-Earth Science and Technology, Yokohama (Japan)
  7. Univ. of Tokyo (Japan). Atmosphere and Ocean Research Inst.
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); Ministry of Education, Culture, Sports, Science and Technology, Japan
OSTI Identifier:
1378974
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Scientific Online Letters on the Atmosphere
Additional Journal Information:
Journal Volume: 12; Journal Issue: 0; Journal ID: ISSN 1349-6476
Publisher:
Meteorological Society of Japan
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; extreme climate and weather events; record-breaking; attribution; climate simulations

Citation Formats

Shiogama, Hideo, Imada, Yukiko, Mori, Masato, Mizuta, Ryo, Stone, Dáithí, Yoshida, Kohei, Arakawa, Osamu, Ikeda, Mikiko, Takahashi, Chiharu, Arai, Miki, Ishii, Masayoshi, Watanabe, Masahiro, and Kimoto, Masahide. Attributing Historical Changes in Probabilities of Record-Breaking Daily Temperature and Precipitation Extreme Events. United States: N. p., 2016. Web. doi:10.2151/sola.2016-045.
Shiogama, Hideo, Imada, Yukiko, Mori, Masato, Mizuta, Ryo, Stone, Dáithí, Yoshida, Kohei, Arakawa, Osamu, Ikeda, Mikiko, Takahashi, Chiharu, Arai, Miki, Ishii, Masayoshi, Watanabe, Masahiro, & Kimoto, Masahide. Attributing Historical Changes in Probabilities of Record-Breaking Daily Temperature and Precipitation Extreme Events. United States. doi:10.2151/sola.2016-045.
Shiogama, Hideo, Imada, Yukiko, Mori, Masato, Mizuta, Ryo, Stone, Dáithí, Yoshida, Kohei, Arakawa, Osamu, Ikeda, Mikiko, Takahashi, Chiharu, Arai, Miki, Ishii, Masayoshi, Watanabe, Masahiro, and Kimoto, Masahide. Sun . "Attributing Historical Changes in Probabilities of Record-Breaking Daily Temperature and Precipitation Extreme Events". United States. doi:10.2151/sola.2016-045. https://www.osti.gov/servlets/purl/1378974.
@article{osti_1378974,
title = {Attributing Historical Changes in Probabilities of Record-Breaking Daily Temperature and Precipitation Extreme Events},
author = {Shiogama, Hideo and Imada, Yukiko and Mori, Masato and Mizuta, Ryo and Stone, Dáithí and Yoshida, Kohei and Arakawa, Osamu and Ikeda, Mikiko and Takahashi, Chiharu and Arai, Miki and Ishii, Masayoshi and Watanabe, Masahiro and Kimoto, Masahide},
abstractNote = {Here, we describe two unprecedented large (100-member), longterm (61-year) ensembles based on MRI-AGCM3.2, which were driven by historical and non-warming climate forcing. These ensembles comprise the "Database for Policy Decision making for Future climate change (d4PDF)". We compare these ensembles to large ensembles based on another climate model, as well as to observed data, to investigate the influence of anthropogenic activities on historical changes in the numbers of record-breaking events, including: the annual coldest daily minimum temperature (TNn), the annual warmest daily maximum temperature (TXx) and the annual most intense daily precipitation event (Rx1day). These two climate model ensembles indicate that human activity has already had statistically significant impacts on the number of record-breaking extreme events worldwide mainly in the Northern Hemisphere land. Specifically, human activities have altered the likelihood that a wider area globally would suffer record-breaking TNn, TXx and Rx1day events than that observed over the 2001- 2010 period by a factor of at least 0.6, 5.4 and 1.3, respectively. However, we also find that the estimated spatial patterns and amplitudes of anthropogenic impacts on the probabilities of record-breaking events are sensitive to the climate model and/or natural-world boundary conditions used in the attribution studies.},
doi = {10.2151/sola.2016-045},
journal = {Scientific Online Letters on the Atmosphere},
number = 0,
volume = 12,
place = {United States},
year = {Sun Aug 07 00:00:00 EDT 2016},
month = {Sun Aug 07 00:00:00 EDT 2016}
}

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