| 来源类型 | Article
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| 规范类型 | 其他
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| DOI | 10.1007/s10584-008-9404-2
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| Learning about parameter and structural uncertainty in carbon cycle models. |
| O'Neill BC; Melnikov NB
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| 发表日期 | 2008
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| 出处 | Climatic Change 89 (1): 23-44
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| 出版年 | 2008
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| 语种 | 英语
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| 摘要 | Uncertainty in the response of the global carbon cycle to anthropogenic emissions plays a key role in assessments of potential future climate change and response strategies. We investigate how fast this uncertainty might change as additional data on the global carbon budget becomes available over the twenty-first century. Using a simple global carbon cycle model and focusing on both parameter and structural uncertainty in the terrestrial sink, we find that additional global data leads to substantial learning (i.e., changes in uncertainty) under some conditions but not others. If the model structure is assumed known and only parameter uncertainty is considered, learning is rather limited if observational errors in the data or themagnitude of unexplained natural variability are not reduced. Learning about parameter values can be substantial, however, when errors in data or unexplained variability are reduced. We also find that, on the one hand, uncertainty in themodel structure has a much bigger impact on uncertainty in projections of future atmospheric composition than does parameter uncertainty. But on the other, it is also possible to learn more about the model structure than the parameter values, even from global budget data that does not improve over time in terms of its associated errors. As an example, we illustrate how one standard model structure, if incorrect, could become inconsistent with global budget data within 40 years. The rate of learning in this analysis is affected by the choice of a relatively simple carbon cycle model, the use of observations only of global emissions and atmospheric concentration, and the assumption of perfect autocorrelation in observational errors and variability. Future work could usefully improve the approach in each of these areas. |
| 主题 | Population and Climate Change (PCC)
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| URL | http://pure.iiasa.ac.at/id/eprint/8591/
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| 来源智库 | International Institute for Applied Systems Analysis (Austria)
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| 引用统计 |
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| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/128867
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推荐引用方式
GB/T 7714 |
O'Neill BC,Melnikov NB. Learning about parameter and structural uncertainty in carbon cycle models.. 2008.
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