Developing statistical climate forecasts for the coming decade

制定未来十年的统计气候预测

• 批准号: NE/H011420/1
• 负责人: Ed Hawkins
• 金额: $ 7.47万
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FH011420%2F1
• 关键词: Developing; statistical; climate; forecasts; coming

Predicting the climate on regional scales for the coming decade would be of considerable value to a wide range of decision makers. For example, information about rainfall would aid planning for water companies, and rail companies could decide whether new track needs to be laid to cope with rising temperatures. Two main factors influence the climate of the next decade; firstly, the continuing response of the climate system to greenhouse gas emissions and other external factors such as volcanoes and solar activity, and secondly, natural fluctuations in the ocean which can offset, or enhance, anthropogenic changes for a decade or two. Until recently, climate models were primarily used to predict only the external component. However, there is now a major international effort underway to add information about the present state of the ocean into climate models in order to consider both factors and hence improve predictions. This project proposes to examine statistical ways to predict the climate over the coming decade, and offers a way to test how good our complex climate models are, and determine whether the resources invested add value to the predictions. The project will focus on predicting sea surface temperatures, but future work could examine air temperature and rainfall predictions.

预测未来十年区域尺度的气候对广大决策者具有相当大的价值。例如，有关降雨的信息将有助于自来水公司的规划，铁路公司可以决定是否需要铺设新的轨道以科普不断上升的气温。有两个主要因素影响今后十年的气候;第一，气候系统对温室气体排放和其他外部因素，如火山和太阳活动的持续反应，第二，海洋的自然波动，它可以抵消或加强十年或二十年的人为变化。直到最近，气候模型主要用于预测外部成分。然而，现在正在进行一项重大的国际努力，将海洋现状的信息添加到气候模型中，以便考虑这两个因素，从而改善预测。该项目旨在研究预测未来十年气候的统计方法，并提供一种方法来测试我们复杂的气候模型有多好，并确定投入的资源是否为预测增加了价值。该项目将侧重于预测海洋表面温度，但未来的工作可能会检查气温和降雨预测。

项目成果

Real-time multi-model decadal climate predictions

• DOI: 10.1007/s00382-012-1600-0
• 发表时间: 2013-12
• 期刊: Climate Dynamics
• 影响因子: 4.6
• 作者: Doug M. Smith;Adam A. Scaife;G. Boer;M. Caian;F. Doblas-Reyes;V. Guemas;E. Hawkins;W. Hazeleger;L. Hermanson;Chun Kit Ho;M. Ishii;V. Kharin;M. Kimoto;B. Kirtman;J. Lean;D. Matei;W. Merryfield;W. Müller;H. Pohlmann;A. Rosati;B. Wouters;K. Wyser

Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions

• DOI: 10.1007/s00382-012-1531-9
• 发表时间: 2013-08-01
• 期刊: CLIMATE DYNAMICS
• 影响因子: 4.6
• 作者: Ho, Chun Kit;Hawkins, Ed;Underwood, Fiona M.
• 通讯作者: Underwood, Fiona M.

Evaluating the potential for statistical decadal predictions of sea surface temperatures with a perfect model approach

• DOI: 10.1007/s00382-011-1023-3
• 发表时间: 2011-03
• 期刊: Climate Dynamics
• 影响因子: 4.6
• 作者: E. Hawkins;J. Robson;R. Sutton;Doug M. Smith;N. Keenlyside

Examining reliability of seasonal to decadal sea surface temperature forecasts: The role of ensemble dispersion

• DOI: 10.1002/2013gl057630
• 发表时间: 2013-11-16
• 期刊: GEOPHYSICAL RESEARCH LETTERS
• 影响因子: 5.2
• 作者: Ho, Chun Kit;Hawkins, Ed;Eade, Rosie
• 通讯作者: Eade, Rosie