DICE/RICE模型中碳循环模块的比较研究

Abstract

碳循环模型的正确构建是影响综合集成评估模型IAM(Integrated Assessment Model)模拟结果的重要因素之一.DICE/RICE模型中的碳循环模型主要有两个,即Nordhaus单层碳库模型和Nordhaus三层碳库模型,但这两个模型的主要缺陷是不考虑陆地生态系统在碳循环中的贡献,因此,引入了包含陆地生态系统的Svirezhev碳循环模型,并将其与Nordhaus单层碳库模型、Nordhaus三层碳库模型展开比较研究.结果表明,在基于历史数据的模型检验中,Svirezhev碳循环模型对全球二氧化碳浓度模拟的准确度优于其他两个模型.对于未来全球气候变化的模拟,3个模型模拟得到了至2100年的温度预测值分别为2.98,3.54,2.91℃,二氧化碳浓度值分别为608.04,733.04,594.70μL/L.其中,Svirezhev碳循环模型的模拟值在3个模型中最低,表明了陆地生态系统和海洋对二氧化碳的吸收作用对抑制全球升温的贡献;而分析也发现Nordhaus三层碳库模型对陆地生态系统和海洋碳库的模拟与实际观测值偏离较大.最后,通过敏感性分析,研究发现DICE/RICE模型中使用的气候响应模块在短期温度模拟中对地表温度的初值较为敏感,在长期温度模拟中敏感度显著下降.总之,从碳循环机制的模拟性能而言,Svirezhev碳循环模型优于其他两个模型,而Nordhaus单层碳库模型虽然机制较为简单却保证了模拟的准确性,但Nordhaus三层碳库模型虽然丰富了碳库的表征,实际上各碳库的模拟准确性差,降低了模型的可靠性.;Modeling of the carbon cycle is one of the most important issues in research of the Integrated Assessment Model (IAM). The carbon-cycle module can not only implement the carbon balance among different carbon reservoirs, but can also provide an interface for climate adaptation and mitigation through management of carbon sinks and land use change. As one of the most popular IAMs in the world, the dynamic integrated model of climate and the economy/regional integrated model of climate and the economy (DICE/RICE) model has two versions of carbon-cycle models. These are the solo-reservoir (N1-N) and three-reservoir (N3-N) models. However, there is an obvious drawback of the two models. This is that terrestrial carbon storage is not considered. Therefore, this work examines the effectiveness of the carbon-cycle models within DICE/RICE, and compares the two models with another carbon-cycle model presented by Svirezhev (S-N model).By inputting global historical emission data into the three models (N1-N, N3-N and S-N), we obtain simulations of historical temperature and CO₂ concentration during 2001 to 2008. The results are calibrated with observed historical CO₂ concentrations and temperature changes, by developing a correlation test. The results show that correlations of CO₂ concentration based on the N1-N, N3-N and S-N models are 0.9967, 0.9971 and 0.9970, respectively, and corresponding correlations of temperature are 0.452, 0.447 and 0.451. It was found that there was a significant correlation between simulated and observed CO₂ concentration data, but simulated and observed temperature data were uncorrelated. This result is verified by an analysis of variance for the simulated and observed data. Although the correlations between the N1-N, N3-N and S-N models are very similar, the standard errors of CO₂ concentration data are 2.53, 2.76 and 0.89, respectively. This shows that the simulation based on the S-N model is much more accurate in relation to the observed data. The N1-N, N3-N and S-N models were used to project climate change by the year 2100, for which the temperatures are 2.98, 3.54℃ and 2.91℃, respectively, and the CO₂ concentrations are 608.04,733.04, 594.70 ppm, respectively. Projections with smallest values were produced by the terrestrial ecosystem and ocean carbon reservoir represented in the S-N model. This indicates that when carbon is absorbed by the terrestrial ecosystem and ocean in that model, atmospheric carbon is less than those in the other models. Although there are also three reservoirs in the DICE/RICE N3-N model, its results deviate substantially from actual observations. The climate response model used by DICE/RICE was also found to be sensitive to the initial value of land surface temperature, when applied in a short-term projection. However, the sensitivity becomes weaker when applied in a long-term projection. Therefore, the S-N model turns out to be superior to the other models in terms of a much more detailed model mechanism and more accurate modeling performance. In spite of the simplification of the N1-N model, its simulation results are still better than those of the N3-N, in which carbon in the biosphere and ocean is significantly different from observations.