Bayesian estimation of climate sensitivity based on a simple climate model fitted to observations of hemispheric temperatures and global ocean heat content
Bayesian estimation of climate sensitivity based on a simple climate model fitted to observations of hemispheric temperatures and global ocean heat content
复制标题
DOI：
10.1002/env.2140
复制
发表时间：
2012-05-01
期刊：
影响因子：
1.7
通讯作者：
Berntsen, Terje Koren
中科院分区：
文献类型：
--
作者：
Aldrin, Magne;Holden, Marit;Berntsen, Terje Koren
对气候变化的预测是不确定的，主要是因为温室气体排放的不确定性，以及气候对大气成分丰度变化的敏感性。平衡气候敏感度是指当气候达到一个新的稳定状态时，由于大气中二氧化碳浓度翻倍而导致的温度升高。二氧化碳只是影响全球气温的几个外部因素中的一个，这些因素统称为辐射强迫机制。本文提出了一种估算气候敏感性的模型框架。该模型的核心是一个基于能量平衡等基本物理定律的简单、确定性的气候模型。它模拟了年度半球表面温度和全球海洋热含量作为历史辐射强迫的函数。这种确定性模型与经验的随机模型相结合，适用于全球温度和海洋热含量的观测，条件是对历史辐射强迫的估计。我们使用贝叶斯框架，具有关于参数子集的信息先验，以及关于气候敏感度和其余参数的平坦先验。用马尔科夫链蒙特卡罗方法对模型进行了估计。版权所有(C)2012 John Wiley&Sons，Ltd.
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 CO2 concentration in the atmosphere when the climate reaches a new steady state. CO2 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 (C) 2012 John Wiley & Sons, Ltd.