Research On Downscaling And Rescaling Of Socio-economic Emission Scenarios

Climate change is a global challenge facing the human society. The accumulationof CO₂and other GHGs in the atmosphere is the major reason that leads to globalwarming and other significant climatic, ecological, and societal changes, and has greatimpact on human being’s lives. Therefore, to project the social, economic and emissiondata under different scenarios is the basis for further comparison and study, as well asfor countries to make emission-reduction pledges and to implement migration andadaptation policies. Up to now, some socio-economic emission scenarios have projectedthe development trends in the future. However, these projections are always provided ata highly aggregated region-by-region basis that can’t provide enough resolution forpolicy analysis at national level, andfiner-scale data are needed for policy analysis byindividual countries. In addition, because of the strong relationships among geography,environment and economy, data atgrid-cell level are also important.To bridge the gap between regional projections from global models andwhat policy analysis requires at national level, this paper introduces, applies andimproves two kinds of downscaling algorithms and one kind of rescalingalgorithm to obtain finer scale data as reference for detailed policy analyses. Onthe side of downscaling, this paper uses downscaling algorithms to disaggregatethe regional data from scenarios to national level, obtaining184countries’ andregions’ data on population, GDP and fossil fuel CO₂emission from2005to2100. On the side of rescaling, this paper revises a set of rescaling algorithmsand implements them to disaggregate the nation-, province-and city-scale dataof population, GDP and CO₂emission in China to the grid level （1°longitude by1°latitude, approximately100km×100km） from2005to2100, obtaining thedata of1093grid cells.Based on the national-level or grid-level data obtained from thedownscaling and rescaling algorithms, as well as the emission-reduction pledgesfrom parties, this paper puts forward several analyses from different angles. Forthe data drived from downscaling algorithms, this paper compares the dataamong countries and regions, and implements the analyses on Business-As-Usual （BAU） scenarios and the scenarios under parties’ pledgesfrom the aspects of equality and temperature increase. For the data drived fromrescaling algorithms, this paper displays the distribution and change rates of thegrid-level data using maps, and the results are combined with the data ontemperature increase and precipitation provided by China’s National ClimateCenter to represent the relationships among the variables.This paper proves the feasibility and reliability of downscaling andrescaling algorithms, and the results can be used as important database forfurther study. In addition, this paper puts forward comparisons and analyses onthe detailed data from several angles, offering reference for policy decision inthe future.