Abstract:

Global warming caused by human activities has devastating impacts on the Earth’s eco-system and the human society. Climate models are the primary tools available for investigating the response of the climate system to various forcings and for making climate change projections into the future. Climate change projections are plagued by various sources of uncertainties, including the greenhouse gases emission scenarios, the internal variability of the climate system, and the representation of the climate processes. To cope with future climate changes, one must quantify those uncertainties properly. Probability distribution is an excellent way to describe the uncertainties. We presented the Bayesian multi-model inference methodology to quantify uncertainty in the climate change projections. We applied this Bayesian framework to assess the climate change projections contained in IPCC-AR5 in the continental China and in two typical large basins in China (Haihe and Pearl River). The results showed that warming is expected all over China under all emissions scenarios. The warming trend from 2006 to 2099 in China is 0.91±0.30 °C/100a, 2.41±0.77 °C/100a, and 6.08±1.01 °C/100a under RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively. Precipitation in China is also projected to be increasing during the 21st century by (5.58±2.96)%/100a, (10.30±4.30)%/100a, and(15.90±6.68)%/100a for the three RCP scenarios, respectively. Under climate change, extreme temperature and precipitation events are projected to be more probable in the future with the probability distribution shifting to the right for both temperature and precipitation.