Simulation And Prediction Of Extreme Precipitation Events In China With Different Cumulus Parameterization Schemes

The ability of climate models to capture extreme precipitation events is crucially important,but most of the existing models contain significant biases for the simulation of extreme precipitation.To understand the causes of these model biases and then improve the simulation and prediction performance,we adopted five different cumulus parameterization schemes in the regional Climate–Weather Research and Forecasting(CWRF)model to investigate its performance and biases in the simulation of extreme precipitation events in China during 1980-2016.We also evaluate the ability of CWRF model for downscaling summer precipitation and atmospheric circulation characteristics in China during 1991-2021 using a return data driven by the BCC second-generation short-range climate forecast model(BCC＿CSM).The main conclusions are summarized as follows:(1)The control experiment of CWRF model using the ensemble cumulus parameterization(ECP)scheme performs better than the ERI on spatial distribution and temporal variation of seasonally averaged general precipitation and extreme precipitation,especially in mountainous and coastal areas showing effective added values.The different cumulus parameterization schemes vary substantially in their model biases and skill scores in different regions and seasons.The ECP scheme was the most skillful in reproducing the spatial distribution of the 95 th percentile daily precipitation(P95)and the other four schemes either overestimated(the Kain-Fritsch Eta and Tiedtke schemes)or underestimated(the Betts-Miller-Janjic and New Simplified Arakawa-Schubert schemes)P95.(2)The cumulus parameterization schemes play an important part in not only the spatial distribution of extreme precipitation,but also the regional mean characteristics of the interannual variation.ECP scheme ranks as the best due to the good simulation of the interannual variation of P95 with the highest temporal correlation coefficient and the smallest root-mean-square error in most areas and seasons,NSAS scheme the second,and KFeta and Tiedtke schemes are third and fourth,respectively.BMJ scheme performs the poorest in reproducing the interannual variation of the observed P95.We computed the temporal correlation coefficients of P95 biases and 22 key factors biases fields such as temperature,radiation,atmospheric circulation,etc.Our results show that,except for a low correlation in Central China,the temporal correlation coefficients(TCCs)between CWRF simulation and MERRA-2 dataset reproduce well the magnitudes and signs of the TCCs between the simulations and observations in most seasons over both North and South China.The MERRA-2 dataset is therefore a good choice for use as reference data in the absence of observational circulation data.(3)To clarify the underlying physical processes of P95 simulation biases,we established a regression model of extreme precipitation based on ECP scheme.This showed that P95 in North China is mainly affected by moisture convergence,planetary boundary layer height and lifting condensation level(relative importance 18–32%).In Central China,the vertical upward motion of water vapor,sensible heat flux and planetary boundary layer height(relative importance 18–30%)are main factors associated with P95.In South China,the vertical upward motion and horizontal transport of water vapor are predominant(relative importance 26–37%).In addition,the net surface energy,surface and atmospheric radiation flux,total precipitable water,convective available potential energy and cloud water path also have a high correlation with P95(the second most important factor;relative importance 14–31%).The influence of each factor on the simulation of P95 is different and the interaction among the different factors determines the ability of CWRF model to simulate extreme precipitation.These results provide important references for future model evaluations and improvements.(4)The performance of CWRF model with different cumulus parameterization schemes(ECP,KFeta and NSAS)for dynamical downscaling China summer precipitaion and atmospheric circulation characteristics are compared and analyzed using the return data driven by the BCC＿CSM model.ECP scheme have the best prediction results for summer precipitation,with minimum standard deviation and high spatial correlation coefficient.The biases between summer NRD,P95,and CDD predicted by the KFeta scheme and the observation in eastern China is the smallest,and NSAS scheme has a better ability to capture the spatial patterns of summer NRD and CDD.In addition,the spatial pattern of the 500 h Pa potential height predicted by the NSAS scheme is closest to ERA5.ECP scheme has the best prediction ability for the location and intensity of the high altitude jet stream at 200 h Pa.However,different cumulus parameterization schemes have relatively small differences in the prediction skills of low altitude wind field at 850 h Pa.Although there are some prediction errors of the atmospheric circulation characteristics between CWRF model using different cumulus parameterization schemes and ERA5 reanalysis data,CWRF model still significantly improves the inaccurate prediction of BCC＿CSM model.The prediction results of summer averaged vertically integrated vapor transport in Central and South China for both CWRF and BCC＿CSM models are not consistent with the actual situation.Therefore,the models need further improvement to enhance their prediction abilities in China.