...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Incorporating model quality information in climate change detection and attribution studies
【24h】

Incorporating model quality information in climate change detection and attribution studies

机译：将模型质量信息纳入气候变化检测和归因研究

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In a recent multimodel detection and attribution (D&A) study using the pooled results from 22 different climate models, the simulated "fingerprint" pattern of anthropogenically caused changes in water vapor was identifiable with high statistical confidence in satellite data. Each model received equal weight in the D&A analysis, despite large differences in the skill with which they simulate key aspects of observed climate. Here, we examine whether water vapor D&A results are sensitive to model quality. The "top 10" and "bottom 10" models are selected with three different sets of skill measures and two different ranking approaches. The entire D&A analysis is then repeated with each of these different sets of more or less skillful models. Our performance metrics include the ability to simulate the mean state, the annual cycle, and the variability associated with El Nino. We find that estimates of an anthropogenic water vapor fingerprint are insensitive to current model uncertainties, and are governed by basic physical processes that are well-represented in climate models. Because the fingerprint is both robust to current model uncertainties and dissimilar to the dominant noise patterns, our ability to identify an anthropogenic influence on observed mul-tidecadal changes in water vapor is not affected by "screening" based on model quality.
机译：在最近的一项多模型检测和归因（D＆A）研究中，使用了来自22种不同气候模型的汇总结果，在卫星数据中具有很高的统计置信度，可以识别出人为引起的水蒸气变化的模拟“指纹”模式。尽管在模拟观测气候关键方面的技巧差异很大，但每个模型在D＆A分析中的权重均相同。在这里，我们检查水蒸气D＆A结果是否对模型质量敏感。使用三组不同的技能度量和两种不同的排名方法选择“前10名”和“后10名”模型。然后，使用这些或多或少熟练的模型的不同集合中的每一个重复整个D＆A分析。我们的绩效指标包括模拟平均状态，年度周期以及与厄尔尼诺现象相关的变异性的能力。我们发现，人为水汽指纹的估计值对当前的模型不确定性不敏感，并且受气候模型中很好表示的基本物理过程的控制。因为指纹既对当前的模型不确定性具有鲁棒性，又与主要的噪声模式不同，所以我们识别人为因素对观察到的水蒸气多方变化的能力不受基于模型质量的“筛选”的影响。

著录项

  • 来源
  • 作者

    B. D. Santer; K. E. Taylor; P. J. Gleckler; C. Bonfils; T. P. Barnett; D. W. Pierce; T. M. L. Wigley; C. Mears; F. J. Wentz; W. Brueggemann; N. P. Gillett; S. A. Klein; S. Solomon; P. A. Stott; M. F. Wehner;

  • 作者单位

    Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550;

    Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550;

    Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550;

    Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550;

    Scripps Institution of Oceanography, La Jolla, CA 92037;

    Scripps Institution of Oceanography, La Jolla, CA 92037;

    National Center for Atmospheric Research, Boulder, CO 80307;

    Remote Sensing Systems, Santa Rosa, CA 95401;

    Remote Sensing Systems, Santa Rosa, CA 95401;

    Institut fuer Unternehmensforschung, Universitat Hamburg, 20146 Hamburg, Germany;

    Canadian Centre for Climate Modelling and Analysis,University of Victoria, Victoria, BC, Canada V8W 3V6;

    Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550;

    Chemical Sciences Division, National Oceanic and Atmospheric Administration Earth System Research Laboratory, Boulder, CO 80305;

    Hadley Centre, U.K. Meteorological Office, Exeter EX1 3PB, United Kingdom;

    Lawrence Berkeley National Laboratory, Berkeley, CA 94720;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    climate modeling; multimodel database; water vapor;

    机译：气候模拟;多模型数据库;水蒸气;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号