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DOI | 10.1007/s10584-018-2232-0 |
Towards Bayesian hierarchical inference of equilibrium climate sensitivity from a combination of CMIP5 climate models and observational data | |
Jonko, Alexandra1; Urban, Nathan M.2; Nadiga, Balu2 | |
2018-07-01 | |
发表期刊 | CLIMATIC CHANGE
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ISSN | 0165-0009 |
EISSN | 1573-1480 |
出版年 | 2018 |
卷号 | 149期号:2页码:247-260 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Despite decades of research, large multi-model uncertainty remains about the Earth's equilibrium climate sensitivity to carbon dioxide forcing as inferred from state-of-the-art Earth system models (ESMs). Statistical treatments of multi-model uncertainties are often limited to simple ESM averaging approaches. Sometimes models are weighted by how well they reproduce historical climate observations. Here, we propose a novel approach to multi-model combination and uncertainty quantification. Rather than averaging a discrete set of models, our approach samples from a continuous distribution over a reduced space of simple model parameters. We fit the free parameters of a reduced-order climate model to the output of each member of the multi-model ensemble. The reduced-order parameter estimates are then combined using a hierarchical Bayesian statistical model. The result is a multi-model distribution of reduced-model parameters, including climate sensitivity. In effect, the multi-model uncertainty problem within an ensemble of ESMs is converted to a parametric uncertainty problem within a reduced model. The multi-model distribution can then be updated with observational data, combining two independent lines of evidence. We apply this approach to 24 model simulations of global surface temperature and net top-of-atmosphere radiation response to abrupt quadrupling of carbon dioxide, and four historical temperature data sets. Our reduced order model is a 2-layer energy balance model. We present probability distributions of climate sensitivity based on (1) the multi-model ensemble alone and (2) the multi-model ensemble and observations. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000439940200010 |
WOS关键词 | SPATIAL-ANALYSIS ; COUPLED CLIMATE ; CARBON-CYCLE ; PROJECTIONS ; OCEAN ; TEMPERATURES ; UNCERTAINTY ; ATMOSPHERE ; ENSEMBLE ; SYSTEM |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/30168 |
专题 | 气候变化 |
作者单位 | 1.Los Alamos Natl Lab, Earth & Environm Sci Div, Los Alamos, NM 87545 USA; 2.Los Alamos Natl Lab, Comp Computat & Stat Sci Div, Los Alamos, NM 87545 USA |
推荐引用方式 GB/T 7714 | Jonko, Alexandra,Urban, Nathan M.,Nadiga, Balu. Towards Bayesian hierarchical inference of equilibrium climate sensitivity from a combination of CMIP5 climate models and observational data[J]. CLIMATIC CHANGE,2018,149(2):247-260. |
APA | Jonko, Alexandra,Urban, Nathan M.,&Nadiga, Balu.(2018).Towards Bayesian hierarchical inference of equilibrium climate sensitivity from a combination of CMIP5 climate models and observational data.CLIMATIC CHANGE,149(2),247-260. |
MLA | Jonko, Alexandra,et al."Towards Bayesian hierarchical inference of equilibrium climate sensitivity from a combination of CMIP5 climate models and observational data".CLIMATIC CHANGE 149.2(2018):247-260. |
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