The meridional gradient of surface air temperature associated with“Warm Arctic–Cold Eurasia”(GradTAE)is closely related to climate anomalies and weather extremes in the mid-low latitudes.However,the Climate Forecas...The meridional gradient of surface air temperature associated with“Warm Arctic–Cold Eurasia”(GradTAE)is closely related to climate anomalies and weather extremes in the mid-low latitudes.However,the Climate Forecast System Version 2(CFSv2)shows poor capability for GradTAE prediction.Based on the year-to-year increment approach,analysis using a hybrid seasonal prediction model for GradTAE in winter(HMAE)is conducted with observed September sea ice over the Barents–Kara Sea,October sea surface temperature over the North Atlantic,September soil moisture in southern North America,and CFSv2 forecasted winter sea ice over the Baffin Bay,Davis Strait,and Labrador Sea.HMAE demonstrates good capability for predicting GradTAE with a significant correlation coefficient of 0.84,and the percentage of the same sign is 88%in cross-validation during 1983−2015.HMAE also maintains high accuracy and robustness during independent predictions of 2016−20.Meanwhile,HMAE can predict the GradTAE in 2021 well as an experiment of routine operation.Moreover,well-predicted GradTAE is useful in the prediction of the large-scale pattern of“Warm Arctic–Cold Eurasia”and has potential to enhance the skill of surface air temperature occurrences in the east of China.展开更多
An enhanced Warm Arctic-Cold Eurasia(WACE)pattern has been a notable feature in recent winters of the Northern Hemisphere.However,divergent results between model and observational studies of the WACE still remain.This...An enhanced Warm Arctic-Cold Eurasia(WACE)pattern has been a notable feature in recent winters of the Northern Hemisphere.However,divergent results between model and observational studies of the WACE still remain.This study evaluates the performance of 39 climate models participating in the Coupled Model Intercomparison Project Phase 6(CMIP6)in simulating the WACE pattern in winter of 1980-2014 and explores the key factors causing the differences in the simulation capability among the models.The results show that the multimodel ensemble(MME)can better simulate the spatial distribution of the WACE pattern than most single models.Models that can/cannot simulate both the climatology and the standard deviation of the Eurasian winter surface air temperature well,especially the latter,usually can/cannot simulate the WACE pattern well.This mainly results from the different abilities of the models to simulate the range and intensity of the warm anomaly in the Barents Sea-Kara seas(BKS)region.Further analysis shows that a good performance of the models in the BKS area is usually related to their ability to simulate location and persistence of Ural blocking(UB),which can transport heat to the BKS region,causing the warm Arctic,and strengthen the westerly trough downstream,cooling central Eurasia.Therefore,simulation of UB is key and significantly affects the model’s performance in simulating the WACE.展开更多
基金This research is supported by the National Key R&D Program of China(Grant No.2022YFF0801604).
文摘The meridional gradient of surface air temperature associated with“Warm Arctic–Cold Eurasia”(GradTAE)is closely related to climate anomalies and weather extremes in the mid-low latitudes.However,the Climate Forecast System Version 2(CFSv2)shows poor capability for GradTAE prediction.Based on the year-to-year increment approach,analysis using a hybrid seasonal prediction model for GradTAE in winter(HMAE)is conducted with observed September sea ice over the Barents–Kara Sea,October sea surface temperature over the North Atlantic,September soil moisture in southern North America,and CFSv2 forecasted winter sea ice over the Baffin Bay,Davis Strait,and Labrador Sea.HMAE demonstrates good capability for predicting GradTAE with a significant correlation coefficient of 0.84,and the percentage of the same sign is 88%in cross-validation during 1983−2015.HMAE also maintains high accuracy and robustness during independent predictions of 2016−20.Meanwhile,HMAE can predict the GradTAE in 2021 well as an experiment of routine operation.Moreover,well-predicted GradTAE is useful in the prediction of the large-scale pattern of“Warm Arctic–Cold Eurasia”and has potential to enhance the skill of surface air temperature occurrences in the east of China.
基金the National Natural Science Foundation of China(Grant Nos.41790471,42075040,and U1902209)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20100304)the National Key Research and Development Program of China(2018YFA0606203,2019YFC1510400).
文摘An enhanced Warm Arctic-Cold Eurasia(WACE)pattern has been a notable feature in recent winters of the Northern Hemisphere.However,divergent results between model and observational studies of the WACE still remain.This study evaluates the performance of 39 climate models participating in the Coupled Model Intercomparison Project Phase 6(CMIP6)in simulating the WACE pattern in winter of 1980-2014 and explores the key factors causing the differences in the simulation capability among the models.The results show that the multimodel ensemble(MME)can better simulate the spatial distribution of the WACE pattern than most single models.Models that can/cannot simulate both the climatology and the standard deviation of the Eurasian winter surface air temperature well,especially the latter,usually can/cannot simulate the WACE pattern well.This mainly results from the different abilities of the models to simulate the range and intensity of the warm anomaly in the Barents Sea-Kara seas(BKS)region.Further analysis shows that a good performance of the models in the BKS area is usually related to their ability to simulate location and persistence of Ural blocking(UB),which can transport heat to the BKS region,causing the warm Arctic,and strengthen the westerly trough downstream,cooling central Eurasia.Therefore,simulation of UB is key and significantly affects the model’s performance in simulating the WACE.
基金the National Natural Science Foundation of China[grant number 42088101]the Postgraduate Research and Practice Innovation Program of Jiangsu Province[grant number KYCX22_1147].
