Bias Correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia

Regional climate models(RCMs)participating in the Coordinated Regional Downscaling Experiment(CORDEX)have been widely used for providing detailed climate change information for specific regions under different emissions scenarios.This study assesses the effects of three common bias correction methods and two multi-model averaging methods in calibrating historical(1980−2005)temperature simulations over East Asia.Future(2006−49)temperature trends under the Representative Concentration Pathway(RCP)4.5 and 8.5 scenarios are projected based on the optimal bias correction and ensemble averaging method.Results show the following:(1)The driving global climate model and RCMs can capture the spatial pattern of annual average temperature but with cold biases over most regions,especially in the Tibetan Plateau region.(2)All bias correction methods can significantly reduce the simulation biases.The quantile mapping method outperforms other bias correction methods in all RCMs,with a maximum relative decrease in root-mean-square error for five RCMs reaching 59.8%(HadGEM3-RA),63.2%(MM5),51.3%(RegCM),80.7%(YSU-RCM)and 62.0%(WRF).(3)The Bayesian model averaging(BMA)method outperforms the simple multi-model averaging(SMA)method in narrowing the uncertainty of bias-corrected results.For the spatial correlation coefficient,the improvement rate of the BMA method ranges from 2%to 31%over the 10 subregions,when compared with individual RCMs.(4)For temperature projections,the warming is significant,ranging from 1.2°C to 3.5°C across the whole domain under the RCP8.5 scenario.(5)The quantile mapping method reduces the uncertainty over all subregions by between 66%and 94%.

An Objective Approach to Generating Multi-Physics Ensemble Precipitation Forecasts Based on the WRF Model

Selecting proper parameterization scheme combinations for a particular application is of great interest to the Weather Research and Forecasting(WRF)model users.This study aims to develop an objective method for identifying a set of scheme combinations to form a multi-physics ensemble suitable for short-range precipitation forecasting in the Greater Beijing area.The ensemble is created by using statistical techniques and some heuristics.An initial sample of 90 scheme combinations was first generated by using Latin hypercube sampling(LHS).Then,after several rounds of screening,a final ensemble of 40 combinations were chosen.The ensemble forecasts generated for both the training and verification cases using these combinations were evaluated based on several verification metrics,including threat score(TS),Brier score(BS),relative operating characteristics(ROC),and ranked probability score(RPS).The results show that TS of the final ensemble improved by 9%-33%over that of the initial ensemble.The reliability was improved for rain≤10 mm day^-1,but decreased slightly for rain>10 mm day^-1 due to insufficient samples.The resolution remained about the same.The final ensemble forecasts were better than that generated from randomly sampled scheme combinations.These results suggest that the proposed approach is an effective way to select a multi-physics ensemble for generating accurate and reliable forecasts.