论文标题
来自统计能源平衡模型的全球温度预测使用多个历史数据来源
Global temperature projections from a statistical energy balance model using multiple sources of historical data
论文作者
论文摘要
本文使用历史数据估计了两组分量平衡模型作为线性状态空间系统（EBM-SS模型）。它是混合层中温度的关节模型，深海层中的温度和辐射强迫。 EBM-SS模型允许在强迫中建模非平稳性，为潜在过程纳入多个数据源以及处理丢失的观测值。我们使用最大可能性在1955 - 2020年的全球级别上使用观察数据集估算EBM -SS模型。我们在经验估计和模拟中显示了对潜在过程的多个数据源可降低参数估计不确定性。 When fitting the EBM-SS model to eight observational global mean surface temperature (GMST) anomaly series, the physical parameter estimates and the GMST projection under Representative Concentration Pathway (RCP) scenarios are comparable to those from Coupled Model Intercomparison Project 5 (CMIP5) models and the climate emulator Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC) 7.5.这提供了证据表明，仅利用简单的气候模型和仅历史记录就可以产生有意义的GMST预测。
This paper estimates the two-component energy balance model as a linear state space system (EBM-SS model) using historical data. It is a joint model for the temperature in the mixed layer, the temperature in the deep ocean layer, and radiative forcing. The EBM-SS model allows for the modeling of non-stationarity in forcing, the incorporation of multiple data sources for the latent processes, and the handling of missing observations. We estimate the EBM-SS model using observational datasets at the global level for the period 1955 - 2020 by maximum likelihood. We show in the empirical estimation and in simulations that using multiple data sources for the latent processes reduces parameter estimation uncertainty. When fitting the EBM-SS model to eight observational global mean surface temperature (GMST) anomaly series, the physical parameter estimates and the GMST projection under Representative Concentration Pathway (RCP) scenarios are comparable to those from Coupled Model Intercomparison Project 5 (CMIP5) models and the climate emulator Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC) 7.5. This provides evidence that utilizing a simple climate model and historical records alone can produce meaningful GMST projections.
