Parameter uncertainty analysis using an EMIC and a terrestrial vegetation model

For quantitative discussions on the effect of anthropogenic CO2 to the Earth system, the uncertainty of model outputs should be discussed with a great care. Ideally, the effect of small perturbations in all accountable conditions to the outputs should be checked. However, as using GCMs it is not realistic to carry out long ensemble runs having a large number of members, a reasonable substitution for such analysis is using Earth system models of intermediate complexity (EMICs). Ensembles, of an EMIC and of a terrestrial vegetation model, with a large number of members will enable us to obtain quantitative information on the uncertainty. MIROC-lite, an EMIC used in this study, is based on MIROC and originally developed in 2001, is consists of an ocean GCM and a 2D energy moisture balance model for atmosphere. Using this model, we carried out an ensemble experiment perturbing 14 parameters at once with 300 members, about 80 % of which survived a 3,000 year run, and then compared the average of air temperature, precipitation, ocean temperature and ocean salinity in the last 100 years with NCEP/NCAR reanalysis or WOA observation data. Consequently, it is found that heat diffusivity plays the most significant role in deciding the spatial distribution of these variables, while for ocean salinity the amount of the freshwater flux adjustment plays as important a role as heat diffusivity. On the other hand, to analyze the uncertainty in a terrestrial vegetation model, multi-parameter ensemble of Sim-CYCLE is designed. A preliminary single-parameter ensemble experiment presented that a 30 % increase/decrease in the maximum photosynthesis rate, the light use efficiency or the coefficient of leaf life expectancy causes a significant change in the total terrestrial ecosystem carbon storage. Moreover, it is revealed that changing a few parameters of the temperature-dependency of soil decomposition results in an even larger change in the ecosystem carbon storage than changing vegetation-related parameters does. At the presentation, results of our global warning experiments combining the two models will also be reported.