[A-CG42] Projection and detection of global environmental change

[ACG42-12] Observational constraints on the simple climate model reduced uncertainties in simulating climate change

*Xuanming Su¹, Kaoru Tachiiri¹ (1.Japan Agency for Marine-Earth Science and Technology)

Keywords:Simple Climate Model, Earth System Model, observational constraints, uncertainty in climate change simulations

Regarding climate change modeling, with the rapid development of computers and the improvement of related simulation methods, the simulation of climate change has been improved to a relatively higher level. However, there are still non-negligible uncertainties in the simulation of historical climate change, which could present a very likely range as wider as 1.0 ℃ for recent temperature increases. The simple climate model, which is usually used to represent the behaviors of complex Earth System Models (ESMs), could possibly produce a larger uncertainty. This poses a challenge to the prediction of future climate change and the formulation of relevant climate change policies. In this study, we used the Simple Climate Model for Optimization version 3.3 (SCM4OPT v3.3) to simulate the main behaviors of ESMs. The SCM4OPT v3.3 has a carbon cycle module that models the carbon cycles on land and in the ocean, an atmospheric chemistry module that models how emissions of non-CO₂ and aerosols and pollutants change the atmosphere, and a climate module that models how temperatures change in response to radiative forcing. During the calibration, we used the observed atmospheric CO₂ concentration and temperature record as constraints. The results showed that the observational constraints on the simple climate model could reduce the uncertainty to +/- 0.2 ℃ for recent year’s temperature increases, even though the climate effects resulting from aerosol forcing were also considered, which might cause a relatively large uncertainty. This suggests that using observations to constrain simulations of simple climate models can be effective in improving simulation accuracy and reducing uncertainty.