The coupling between wind stress perturbations and sea surface temperature(SST)perturbations induced by tropical instability waves(TIWs)in the Pacific Ocean has been revealed previously and proven crucial to both the ...The coupling between wind stress perturbations and sea surface temperature(SST)perturbations induced by tropical instability waves(TIWs)in the Pacific Ocean has been revealed previously and proven crucial to both the atmosphere and ocean.However,an overlooked fact by previous studies is that the loosely defined“TIWs”actually consist of two modes,including the Yanai wave-based TIW on the equator(hereafter eTIW)and the Rossby wave-based TIW off the equator(hereafter vTIW).Hence,the individual feedbacks of the wind stress to the bimodal TIWs remain unexplored.In this study,individual coupling relationships are established for both eTIW and v TIW,including the relationship between the TIW-induced SST perturbations and two components of wind stress perturbations,and the relationship between the TIW-induced wind stress perturbation divergence(curl)and the downwind(crosswind)TIW-induced SST gradients.Results show that,due to different distributions of eTIW and vTIW,the coupling strength induced by the eTIW is stronger on the equator,and that by the vTIW is stronger off the equator.The results of any of eTIW and vTIW are higher than those of the loosely defined TIWs.We further investigated how well the coupling relationships remained in several widely recognized oceanic general circulation models and fully coupled climate models.However,the coupling relationships cannot be well represented in most numerical models.Finally,we confirmed that higher resolution usually corresponds to more accurate simulation.Therefore,the coupling models established in this study are complementary to previous research and can be used to refine the oceanic and coupled climate models.展开更多
As important atmospheric circulation patterns in Northern Hemisphere(NH),the North Atlantic Oscillation(NAO)and the Western Pacific teleconnection(WP)affect the winter climate in Eurasia.In order to explore the combin...As important atmospheric circulation patterns in Northern Hemisphere(NH),the North Atlantic Oscillation(NAO)and the Western Pacific teleconnection(WP)affect the winter climate in Eurasia.In order to explore the combined effects of NAO and WP on East Asian(EA)temperature,the NAO and WP indices are divided into four phases from 1980−2021:the positive NAO and WP phase(NAO+/WP+),the negative NAO and WP phase(NAO−/WP−),the positive NAO and negative WP phase(NAO+/WP−),the negative NAO and positive WP phase(NAO−/WP+).In the phase of NAO+/WP+,the low geopotential height(GH)stays in north of EA at 50°−80°N;the surface air temperature anomaly(SATA)is 0.8−1℃lower than Southern Asian.In the phase of NAO−/WP−,the center of high temperature and GH locate in the northeast of EA;the cold air spreads to Southern Asia,causing the SATA decreases 1−1.5℃.In the phase of NAO+/WP−,the high GH belt is formed at 55°−80°N.Meanwhile,the center of high SATA locates in the north of Asia that increases 0.8−1.1℃.The cold airflow causes temperature dropping 0.5−1℃in the south of EA.The SATA improves 0.5−1.5℃in south of EA in the phase of NAO−/WP+.The belt of high GH is formed at 25°−50°N,and blocks the cold air which from Siberia.The NAO and WP generate two warped plate pressure structures in NH,and affect the temperature by different pressure configurations.NAO and WP form different GH,and GH acts to block and push airflow by affecting the air pressure,then causes the temperature to be different from the north and south of EA.Finally,the multiple linear regression result shows that NAO and WP are weakened by each other such as the phase of NAO+/WP+and NAO−/WP−.展开更多
Objective:To study the application of a machine learning algorithm for predicting gestational diabetes mellitus(GDM)in early pregnancy.Methods:This study identified indicators related to GDM through a literature revie...Objective:To study the application of a machine learning algorithm for predicting gestational diabetes mellitus(GDM)in early pregnancy.Methods:This study identified indicators related to GDM through a literature review and expert discussion.Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis,and the collected indicators were retrospectively analyzed.Based on Python,the indicators were classified and modeled using a random forest regression algorithm,and the performance of the prediction model was analyzed.Results:We obtained 4806 analyzable data from 1625 pregnant women.Among these,3265 samples with all 67 indicators were used to establish data set F1;4806 samples with 38 identical indicators were used to establish data set F2.Each of F1 and F2 was used for training the random forest algorithm.The overall predictive accuracy of the F1 model was 93.10%,area under the receiver operating characteristic curve(AUC)was 0.66,and the predictive accuracy of GDM-positive cases was 37.10%.The corresponding values for the F2 model were 88.70%,0.87,and 79.44%.The results thus showed that the F2 prediction model performed better than the F1 model.To explore the impact of sacrificial indicators on GDM prediction,the F3 data set was established using 3265 samples(F1)with 38 indicators(F2).After training,the overall predictive accuracy of the F3 model was 91.60%,AUC was 0.58,and the predictive accuracy of positive cases was 15.85%.Conclusions:In this study,a model for predicting GDM with several input variables(e.g.,physical examination,past history,personal history,family history,and laboratory indicators)was established using a random forest regression algorithm.The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy.In addition,there are cer tain requirements for the propor tions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.展开更多
Autumn Arctic sea ice has been declining since the beginning of the era of satellite sea ice observations.In this study,we examined the factors contributing to the decline of autumn sea ice concentration.From the Beau...Autumn Arctic sea ice has been declining since the beginning of the era of satellite sea ice observations.In this study,we examined the factors contributing to the decline of autumn sea ice concentration.From the Beaufort Sea to the Barents Sea,autumn sea ice concentration has decreased considerably between 1982 and 2020,and the rates of decline were the highest around the Beaufort Sea.We calculated the correlation coefficients between sea ice extent(SIE)anomalies and anomalies of sea surface temperature(SST),surface air temperature(SAT)and specific humidity(SH).Among these coefficients,the largest absolute value was found in the coefficient between SIE and SAT anomalies for August to October,which has a value of−0.9446.The second largest absolute value was found in the coefficient between SIE and SH anomalies for September to November,which has a value of−0.9436.Among the correlation coefficients between SIE and SST anomalies,the largest absolute value was found in the coefficient for August to October,which has a value of−0.9410.We conducted empirical orthogonal function(EOF)analyses of sea ice,SST,SAT,SH,sea level pressure(SLP)and the wind field for the months where the absolute values of the correlation coefficient were the largest.The first EOFs of SST,SAT and SH account for 39.07%,63.54%and 47.60%of the total variances,respectively,and are mainly concentrated in the area between the Beaufort Sea and the East Siberian Sea.The corresponding principal component time series also indicate positive trends.The first EOF of SLP explains 41.57%of the total variance.It is mostly negative in the central Arctic.Over the Beaufort,Chukchi and East Siberian seas,the zonal wind weakened while the meridional wind strengthened.Results from the correlation and EOF analyses further verified the effects of the ice-temperature,ice-SH and ice-SLP feedback mechanisms in the Arctic.These mechanisms accelerate melting and decrease the rate of formation of sea ice.In addition,stronger meridional winds favor the flow of warm air from lower latitudes towards the polar region,further promoting Arctic sea ice decline.展开更多
基金Supported by the National Natural Science Foundation of China(No.41976012)the Key Research Program of Laoshan Laboratory(LSL)(No.LSKJ 202202502)the Strategic Priority Research Program of Chinese Academy of Sciences(CAS)(No.XDB 42000000)。
文摘The coupling between wind stress perturbations and sea surface temperature(SST)perturbations induced by tropical instability waves(TIWs)in the Pacific Ocean has been revealed previously and proven crucial to both the atmosphere and ocean.However,an overlooked fact by previous studies is that the loosely defined“TIWs”actually consist of two modes,including the Yanai wave-based TIW on the equator(hereafter eTIW)and the Rossby wave-based TIW off the equator(hereafter vTIW).Hence,the individual feedbacks of the wind stress to the bimodal TIWs remain unexplored.In this study,individual coupling relationships are established for both eTIW and v TIW,including the relationship between the TIW-induced SST perturbations and two components of wind stress perturbations,and the relationship between the TIW-induced wind stress perturbation divergence(curl)and the downwind(crosswind)TIW-induced SST gradients.Results show that,due to different distributions of eTIW and vTIW,the coupling strength induced by the eTIW is stronger on the equator,and that by the vTIW is stronger off the equator.The results of any of eTIW and vTIW are higher than those of the loosely defined TIWs.We further investigated how well the coupling relationships remained in several widely recognized oceanic general circulation models and fully coupled climate models.However,the coupling relationships cannot be well represented in most numerical models.Finally,we confirmed that higher resolution usually corresponds to more accurate simulation.Therefore,the coupling models established in this study are complementary to previous research and can be used to refine the oceanic and coupled climate models.
基金The National Key Research and Development Program of China under contract No.2022YFE0140500the National Natural Science Foundation of China under contract Nos 41821004 and 42130406+2 种基金the National Natural Science Foundation of China-Shandong Joint Fund under contract No.U1906215the Open Fund of Key Laboratory of Ocean Circulation and Waves,Chinese Academy of Sciences under contract No.KLOCW2003the Project of Doctoral Found of Qingdao University of Science and Technology under contract No.210010022746.
文摘As important atmospheric circulation patterns in Northern Hemisphere(NH),the North Atlantic Oscillation(NAO)and the Western Pacific teleconnection(WP)affect the winter climate in Eurasia.In order to explore the combined effects of NAO and WP on East Asian(EA)temperature,the NAO and WP indices are divided into four phases from 1980−2021:the positive NAO and WP phase(NAO+/WP+),the negative NAO and WP phase(NAO−/WP−),the positive NAO and negative WP phase(NAO+/WP−),the negative NAO and positive WP phase(NAO−/WP+).In the phase of NAO+/WP+,the low geopotential height(GH)stays in north of EA at 50°−80°N;the surface air temperature anomaly(SATA)is 0.8−1℃lower than Southern Asian.In the phase of NAO−/WP−,the center of high temperature and GH locate in the northeast of EA;the cold air spreads to Southern Asia,causing the SATA decreases 1−1.5℃.In the phase of NAO+/WP−,the high GH belt is formed at 55°−80°N.Meanwhile,the center of high SATA locates in the north of Asia that increases 0.8−1.1℃.The cold airflow causes temperature dropping 0.5−1℃in the south of EA.The SATA improves 0.5−1.5℃in south of EA in the phase of NAO−/WP+.The belt of high GH is formed at 25°−50°N,and blocks the cold air which from Siberia.The NAO and WP generate two warped plate pressure structures in NH,and affect the temperature by different pressure configurations.NAO and WP form different GH,and GH acts to block and push airflow by affecting the air pressure,then causes the temperature to be different from the north and south of EA.Finally,the multiple linear regression result shows that NAO and WP are weakened by each other such as the phase of NAO+/WP+and NAO−/WP−.
基金supported by the Qingdao Municipal Bureau of Science and Technology(No.19-6-1-55-nsh)。
文摘Objective:To study the application of a machine learning algorithm for predicting gestational diabetes mellitus(GDM)in early pregnancy.Methods:This study identified indicators related to GDM through a literature review and expert discussion.Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis,and the collected indicators were retrospectively analyzed.Based on Python,the indicators were classified and modeled using a random forest regression algorithm,and the performance of the prediction model was analyzed.Results:We obtained 4806 analyzable data from 1625 pregnant women.Among these,3265 samples with all 67 indicators were used to establish data set F1;4806 samples with 38 identical indicators were used to establish data set F2.Each of F1 and F2 was used for training the random forest algorithm.The overall predictive accuracy of the F1 model was 93.10%,area under the receiver operating characteristic curve(AUC)was 0.66,and the predictive accuracy of GDM-positive cases was 37.10%.The corresponding values for the F2 model were 88.70%,0.87,and 79.44%.The results thus showed that the F2 prediction model performed better than the F1 model.To explore the impact of sacrificial indicators on GDM prediction,the F3 data set was established using 3265 samples(F1)with 38 indicators(F2).After training,the overall predictive accuracy of the F3 model was 91.60%,AUC was 0.58,and the predictive accuracy of positive cases was 15.85%.Conclusions:In this study,a model for predicting GDM with several input variables(e.g.,physical examination,past history,personal history,family history,and laboratory indicators)was established using a random forest regression algorithm.The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy.In addition,there are cer tain requirements for the propor tions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.
基金the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(Grant no.2018SDKJ0106-1)Open Fund of the Key Laboratory of Ocean Circulation and Waves,Chinese Academy of Sciences(Grant no.KLOCW2003)the Project of Doctoral Found of Qingdao University of Science and Technology(Grant no.210010022746)。
文摘Autumn Arctic sea ice has been declining since the beginning of the era of satellite sea ice observations.In this study,we examined the factors contributing to the decline of autumn sea ice concentration.From the Beaufort Sea to the Barents Sea,autumn sea ice concentration has decreased considerably between 1982 and 2020,and the rates of decline were the highest around the Beaufort Sea.We calculated the correlation coefficients between sea ice extent(SIE)anomalies and anomalies of sea surface temperature(SST),surface air temperature(SAT)and specific humidity(SH).Among these coefficients,the largest absolute value was found in the coefficient between SIE and SAT anomalies for August to October,which has a value of−0.9446.The second largest absolute value was found in the coefficient between SIE and SH anomalies for September to November,which has a value of−0.9436.Among the correlation coefficients between SIE and SST anomalies,the largest absolute value was found in the coefficient for August to October,which has a value of−0.9410.We conducted empirical orthogonal function(EOF)analyses of sea ice,SST,SAT,SH,sea level pressure(SLP)and the wind field for the months where the absolute values of the correlation coefficient were the largest.The first EOFs of SST,SAT and SH account for 39.07%,63.54%and 47.60%of the total variances,respectively,and are mainly concentrated in the area between the Beaufort Sea and the East Siberian Sea.The corresponding principal component time series also indicate positive trends.The first EOF of SLP explains 41.57%of the total variance.It is mostly negative in the central Arctic.Over the Beaufort,Chukchi and East Siberian seas,the zonal wind weakened while the meridional wind strengthened.Results from the correlation and EOF analyses further verified the effects of the ice-temperature,ice-SH and ice-SLP feedback mechanisms in the Arctic.These mechanisms accelerate melting and decrease the rate of formation of sea ice.In addition,stronger meridional winds favor the flow of warm air from lower latitudes towards the polar region,further promoting Arctic sea ice decline.
