文章基本信息

标题：Learning from global emissions scenarios
本地全文：下载
作者：Brian C O’Neill ; Nebojsa Nakicenovic
期刊名称：Environmental Research Letters
印刷版ISSN：1748-9326
电子版ISSN：1748-9326
出版年度：2008
卷号：3
期号：4
页码：045014-045014
DOI：10.1088/1748-9326/3/4/045014
语种：English
出版社：IOP Publishing Ltd
摘要：Scenarios of global greenhouse gas emissions have played a key role in climate change analysis for over twenty years. Currently, several research communities are organizing to undertake a new round of scenario development in the lead-up to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). To help inform this process, we assess a number of past efforts to develop and learn from sets of global greenhouse gas emissions scenarios. We conclude that while emissions scenario exercises have likely had substantial benefits for participating modeling teams and produced insights from individual models, learning from the exercises taken as a whole has been more limited. Model comparison exercises have typically focused on the production of large numbers of scenarios while investing little in assessing the results or the production process, perhaps on the assumption that later assessment efforts could play this role. However, much of this assessment potential remains untapped. Efforts such as scenario-related chapters of IPCC reports have been most informative when they have gone to extra lengths to carry out more specific comparison exercises, but in general these assessments do not have the remit or resources to carry out the kind of detailed analysis of scenario results necessary for drawing the most useful conclusions. We recommend that scenario comparison exercises build-in time and resources for assessing scenario results in more detail at the time when they are produced, that these exercises focus on more specific questions to improve the prospects for learning, and that additional scenario assessments are carried out separately from production exercises. We also discuss the obstacles to better assessment that might exist, and how they might be overcome. Finally, we recommend that future work include much greater emphasis on understanding how scenarios are actually used, as a guide to improving scenario production.