Emergent Constraints on Future Changes in Several Climate Variables and Extreme Indices from Global to Regional Scales

抄録

Climate change impact modelling studies often require not only mean temperature and precipitation but also other climate variables (e.g., solar radiation and wind speed) and extreme indices as input data. However, studies on observational constraints (emergent constraints) about these variables and indices are limited. Based on linearities of future climate change as functions of global warming levels and biases in recent global mean temperature trends in the simulations of 40 Earth system models (ESMs), the upper bounds of uncertainties in future changes of various variables (annual mean temperature, annual maximum daily maximum temperature, mean specific humidity, mean downward longwave radiation and specific humidity on days when annual maximum daily precipitation (Rx1day) events occur) are successfully lowered in most regions of the world. We can also reduce inter-model variances of regional changes in mean precipitation, Rx1day, mean downward shortwave radiation, mean sea level pressure and mean surface wind speed in some areas. These results would be useful for climate change impact studies to consider whether they should weight ESMs or exclude some ESMs to prevent possible biases in impact assessments.