The instrumental temperature records are affected by both external climate forcings—in particular, the increase of long-lived greenhouse gas emissions—and natural, internal variability. Estimates of the value of equ...The instrumental temperature records are affected by both external climate forcings—in particular, the increase of long-lived greenhouse gas emissions—and natural, internal variability. Estimates of the value of equilibrium climate sensitivity—the change in global-mean equilibrium near-surface temperature due to a doubling of the pre-industrial CO2 concentration—and other climate parameters using these observational records are affected by the presence of the internal variability. A different realization of the natural variability will result in different estimates of the values of these climate parameters. In this study we apply Bayesian estimation to simulated temperature and ocean heat-uptake records generated by our Climate Research Group’s Simple Climate Model for known values of equilibrium climate sensitivity, T2x direct sulfate aerosol forcing in reference year 2000, FASA, and oceanic heat diffusivity, ΔT2x. We choose the simulated records for one choice of values of the climate parameters to serve as the synthetic observations. To each of the simulated temperature records we add a number of draws of the quasi-periodic oscillations and stochastic noise, determined from the observed temperature record. For cases considering only values of ΔT2x and/or κ, the Bayesian estimation converges to the value(s) of ΔT2x and/or κ used to generate the synthetic observations. However, for cases studying FASA, the Bayesian analysis does not converge to the “true” value used to generate the synthetic observations. We show that this is a problem of low signal-to-noise ratio: by substituting an artificial, continuously increasing sulfate record, we greatly improve the value obtained through Bayesian estimation. Our results indicate Bayesian learning techniques will be useful tools in constraining the values of ΔT2x and κ but not FASA In our Group’s future work we will extend the methods used here to the observed, instrumental records of global-mean temperature increase and ocean heat uptake.展开更多
苯并[α]芘[benzo[α]pyrene,BaP]是环境中广泛存在的一种致癌多环芳烃,带来的健康风险受到普遍关注.基于生理的药代动力学(physiologically based pharmacokinetic, PBPK)模型是一种预测污染物在生物体内部剂量的数学模型,近年来在健...苯并[α]芘[benzo[α]pyrene,BaP]是环境中广泛存在的一种致癌多环芳烃,带来的健康风险受到普遍关注.基于生理的药代动力学(physiologically based pharmacokinetic, PBPK)模型是一种预测污染物在生物体内部剂量的数学模型,近年来在健康风险评估中应用广泛.本文介绍了BaP对生物体的健康危害,概述了BaP的PBPK模型研究进展,指出了BaP人体PBPK模型存在BaP及代谢物的代谢机理尚未完全明确、代谢参数可靠性不高、模型还需继续完善等问题,并探讨了PBPK模型在BaP健康风险评估中的应用.一方面,PBPK模型在阐明内暴露监测结果及补充完善污染物在人体内的代谢机理方面具有明显优势,基于PBPK模型分析完善了BaP生物标志物3-羟基苯并[α]芘在肾小管重吸收的肾脏排泄机制;另一方面,PBPK模型作为外推工具,通过种间外推可以量化污染物的种间药代动力学差异,减小动物健康剂量水平外推至人体基准值的不确定性;通过体外到体内的外推可以关联内外暴露剂量,利用反剂量学推导人体健康基准值.这两种外推方法的应用均可以提高人体健康基准值推导的科学性、准确性.并以BaP为例剖析了PBPK模型不确定性来源,提出了提高模型精确性的方法.最后,为了进一步推动完善BaP的人体健康风险评估方法体系,本文探讨总结了3个重点研究方向:一是探索PBPK模型应用于BaP健康风险评估的方法体系;二是探索可靠性更高的Ba P健康风险评估概率模型;三是开展BaP的生物标志物用于人体健康风险评估可行性研究.展开更多
文摘The instrumental temperature records are affected by both external climate forcings—in particular, the increase of long-lived greenhouse gas emissions—and natural, internal variability. Estimates of the value of equilibrium climate sensitivity—the change in global-mean equilibrium near-surface temperature due to a doubling of the pre-industrial CO2 concentration—and other climate parameters using these observational records are affected by the presence of the internal variability. A different realization of the natural variability will result in different estimates of the values of these climate parameters. In this study we apply Bayesian estimation to simulated temperature and ocean heat-uptake records generated by our Climate Research Group’s Simple Climate Model for known values of equilibrium climate sensitivity, T2x direct sulfate aerosol forcing in reference year 2000, FASA, and oceanic heat diffusivity, ΔT2x. We choose the simulated records for one choice of values of the climate parameters to serve as the synthetic observations. To each of the simulated temperature records we add a number of draws of the quasi-periodic oscillations and stochastic noise, determined from the observed temperature record. For cases considering only values of ΔT2x and/or κ, the Bayesian estimation converges to the value(s) of ΔT2x and/or κ used to generate the synthetic observations. However, for cases studying FASA, the Bayesian analysis does not converge to the “true” value used to generate the synthetic observations. We show that this is a problem of low signal-to-noise ratio: by substituting an artificial, continuously increasing sulfate record, we greatly improve the value obtained through Bayesian estimation. Our results indicate Bayesian learning techniques will be useful tools in constraining the values of ΔT2x and κ but not FASA In our Group’s future work we will extend the methods used here to the observed, instrumental records of global-mean temperature increase and ocean heat uptake.
文摘苯并[α]芘[benzo[α]pyrene,BaP]是环境中广泛存在的一种致癌多环芳烃,带来的健康风险受到普遍关注.基于生理的药代动力学(physiologically based pharmacokinetic, PBPK)模型是一种预测污染物在生物体内部剂量的数学模型,近年来在健康风险评估中应用广泛.本文介绍了BaP对生物体的健康危害,概述了BaP的PBPK模型研究进展,指出了BaP人体PBPK模型存在BaP及代谢物的代谢机理尚未完全明确、代谢参数可靠性不高、模型还需继续完善等问题,并探讨了PBPK模型在BaP健康风险评估中的应用.一方面,PBPK模型在阐明内暴露监测结果及补充完善污染物在人体内的代谢机理方面具有明显优势,基于PBPK模型分析完善了BaP生物标志物3-羟基苯并[α]芘在肾小管重吸收的肾脏排泄机制;另一方面,PBPK模型作为外推工具,通过种间外推可以量化污染物的种间药代动力学差异,减小动物健康剂量水平外推至人体基准值的不确定性;通过体外到体内的外推可以关联内外暴露剂量,利用反剂量学推导人体健康基准值.这两种外推方法的应用均可以提高人体健康基准值推导的科学性、准确性.并以BaP为例剖析了PBPK模型不确定性来源,提出了提高模型精确性的方法.最后,为了进一步推动完善BaP的人体健康风险评估方法体系,本文探讨总结了3个重点研究方向:一是探索PBPK模型应用于BaP健康风险评估的方法体系;二是探索可靠性更高的Ba P健康风险评估概率模型;三是开展BaP的生物标志物用于人体健康风险评估可行性研究.
