Implications from Subseasonal Prediction Skills of the Prolonged Heavy Snow Event over Southern China in Early 2008

An exceptionally prolonged heavy snow event(PHSE)occurred in southern China from 10 January to 3 February 2008,which caused considerable economic losses and many casualties.To what extent any dynamical model can predict such an extreme event is crucial for disaster prevention and mitigation.Here,we found the three S2S models(ECMWF,CMA1.0 and CMA2.0)can predict the distribution and intensity of precipitation and surface air temperature(SAT)associated with the PHSE at 10-day lead and 10−15-day lead,respectively.The success is attributed to the models’capability in forecasting the evolution of two important low-frequency systems in the tropics and mid-latitudes[the persistent Siberian High and the suppressed phase of the Madden−Julian Oscillation(MJO)],especially in the ECMWF model.However,beyond the 15-day lead,the three models show almost no skill in forecasting this PHSE.The bias in capturing the two critical circulation systems is responsible for the low skill in forecasting the 2008 PHSE beyond the 15-day lead.On one hand,the models cannot reproduce the persistence of the Siberian High,which results in the underestimation of negative SAT anomalies over southern China.On the other hand,the models cannot accurately capture the suppressed convection of the MJO,leading to weak anomalous southerly and moisture transport,and therefore the underestimation of precipitation over southern China.The Singular Value Decomposition(SVD)analyses between the critical circulation systems and SAT/precipitation over southern China shows a robust historical relation,indicating the fidelity of the predictability sources for both regular events and extreme events(e.g.,the 2008 PHSE).

Sub-Seasonal Predictability of the Northeast China Cold Vortex in BCC and ECMWF S2S Model Forecasts for 2006-2021

As an important atmospheric circulation system in the mid-high latitudes of East Asia,the Northeast China cold vortex(NCCV)substantially influences weather and climate in this region.So far,systematic assessment on the performance of numerical prediction of the NCCVs has not been carried out.Based on the Beijing Climate Centre(BCC)and the ECMWF model hindcast and forecast data that participated in the Sub-seasonal to Seasonal(S2S)Prediction Project,this study systematically examines the performance of both models in simulating and forecasting the NCCVs at the sub-seasonal timescale.The results demonstrate that the two models can effectively capture the seasonal variations in the intensity,active days,and spatial distribution of NCCVs;however,the duration of NCCVs is shorter and the intensity is weaker in the models than in the observations.Diagnostic analysis shows that the differences in the intensity and location of the East Asian subtropical westerly jet and the wave train pattern from North Atlantic to East Asia may be responsible for the deficient simulation of NCCV events in the S2S models.Nonetheless,in the deterministic forecasts,BCC and ECMWF provide skillful prediction on the anomalous numbers of NCCV days and intensity at a lead time of 4-5(5-6)pentads,and the skill limit of the ensemble mean is 1-2 pentads longer than that of individual members.In the probabilistic forecasts of daily NCCV activities,BCC and ECMWF exhibit a forecasting skill of approximately 7 and 11 days,respectively;both models show seasonal dependency in the simulation performance and forecast skills of NCCV events,with better performance in winter than in summer.The results from this study provide helpful references for further improvement of the S2S prediction of NCCVs.

Intraseasonal Variability of Summertime Surface Air Temperature over Mid-High-Latitude Eurasia and Its Prediction Skill in S2S Models

Features of the dominant modes of surface air temperature(SAT)on the intraseasonal timescale over the mid-highlatitude Eurasia(MHE)during boreal summer(June-September)are investigated based on the ERA5 reanalysis data from 1979 to 2016.The intraseasonal variability(ISV)of SAT over MHE is primarily characterized by an eastward propagation along 60°N,which is found to impact the regional weather in China,including summertime extreme hot and cool events.The forecast skill and potential predictability of the ISV of SAT over MHE are assessed for 5 dynamical models that have participated in the subseasonal-to-seasonal(S2 S)prediction project,by analyzing12 years’(1999-2010)model reforecast/hindcast data.By using the principal component(PC)index of the leading intraseasonal SAT modes as a predictand,we found that the forecast skill for ISV of SAT can reach out to 11-17 days,and the ECMWF model exhibits the best score.All the S2 S models tend to show 1)a relatively higher skill for strong intraseasonal oscillation(ISO)cases,2)a systematic underestimate of the amplitude of the SAT ISV signal,and 3)different skills during different phases of ISO cases.Analysis of potential predictability based on the perfectmodel assumption reveals a 4-6-day skill gap for most models,and the skill gap also varies among different phases of ISO events.The results imply the need for continued development of operational forecasting systems to improve the actual prediction skills for the ISV of SAT over MHE.