Bayesian estimation of climate sensitivity based on a simple climate model fitted to observations of hemispheric temperatures and global ocean heat content
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- Magne Aldrin
- Norwegian Computing Center Norway
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- Marit Holden
- Norwegian Computing Center Norway
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- Peter Guttorp
- Norwegian Computing Center Norway
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- Ragnhild Bieltvedt Skeie
- Center for International Climate and Environmental Research ‐ Oslo Oslo Norway
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- Gunnar Myhre
- Center for International Climate and Environmental Research ‐ Oslo Oslo Norway
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- Terje Koren Berntsen
- Center for International Climate and Environmental Research ‐ Oslo Oslo Norway
書誌事項
- 公開日
- 2012-02-24
- 権利情報
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- http://onlinelibrary.wiley.com/termsAndConditions#vor
- DOI
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- 10.1002/env.2140
- 公開者
- Wiley
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説明
<jats:p>Predictions of climate change are uncertain mainly because of uncertainties in the emissions of greenhouse gases and how sensitive the climate is to changes in the abundance of the atmospheric constituents. The equilibrium climate sensitivity is defined as the temperature increase because of a doubling of the CO<jats:sub>2</jats:sub> concentration in the atmosphere when the climate reaches a new steady state. CO<jats:sub>2</jats:sub> is only one out of the several external factors that affect the global temperature, called radiative forcing mechanisms as a collective term. In this paper, we present a model framework for estimating the climate sensitivity. The core of the model is a simple, deterministic climate model based on elementary physical laws such as energy balance. It models yearly hemispheric surface temperature and global ocean heat content as a function of historical radiative forcing. This deterministic model is combined with an empirical, stochastic model and fitted to observations on global temperature and ocean heat content, conditioned on estimates of historical radiative forcing. We use a Bayesian framework, with informative priors on a subset of the parameters and flat priors on the climate sensitivity and the remaining parameters. The model is estimated by Markov Chain Monte Carlo techniques. Copyright © 2012 John Wiley & Sons, Ltd.</jats:p>
収録刊行物
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- Environmetrics
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Environmetrics 23 (3), 253-271, 2012-02-24
Wiley
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詳細情報 詳細情報について
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- CRID
- 1361699996336537600
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- DOI
- 10.1002/env.2140
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- ISSN
- 1099095X
- 11804009
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- データソース種別
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- Crossref
