Previous studies have indicated a global reversal of near-surface wind speeds from a declining trend to an increasing trend around 2010;however,it remains unclear whether upper-air wind speeds exhibit a similar revers...Previous studies have indicated a global reversal of near-surface wind speeds from a declining trend to an increasing trend around 2010;however,it remains unclear whether upper-air wind speeds exhibit a similar reversal.This study evaluates reanalysis products using surface and radiosonde observations to analyze upper-air wind speed variations in the Northern Hemisphere,focusing on their seasonal and latitudinal differences.Results demonstrate that JRA-55 effectively captures wind speed variations in the Northern Hemisphere.Notably,upper-air wind speeds over land experienced a reversal in winter 2010 with significant latitudinal differences.The trend reversal of upper wind speed between the midlatitudes and subtropics presents a dipole pattern.From 1990 to 2010,upper-air wind speeds in the midlatitudes(40°-70°N)significantly declined,while the subtropical zone(20°-40°N)displayed an opposite trend.However,during 2010-2020,wind speeds in the midlatitudes shifted to a significant positive trend,whereas the subtropics experienced a significant negative trend.The variations in Northern Hemisphere winter wind speeds can be attributed to changes in low-level baroclinicity driven by tropical diabatic heating and midlatitude transient eddy feedback.Enhanced diabatic heating and weakened eddy feedback during 1990-2010 contributed to reduced wind speeds in the midlatitudes and increased speeds in the subtropics,while reduced diabatic heating and strengthened eddy feedback during 2010-2020 resulted in increased wind speeds in the midlatitudes and decreased speeds in the subtropics.The reversal of upper-air wind speeds could affect surface wind speeds by downward momentum transfer,which could contribute to the reversal of surface wind speeds.展开更多
Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the nume...Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.展开更多
基金supported by the National Natural Science Foundation of China[grant numbers U2442207,42122034,42075043,and 42330609]the Youth Innovation Promotion Association[grant number 2021427]+2 种基金the West Light Foundation[grant number xbzgzdsys-202409]of the Chinese Academy of Sciencesthe Key Talent Projects in Gansu Provincethe Central Guidance Fund for Local Science and Technology Development Projects in Gansu Province[grant number 24ZYQA031].
文摘Previous studies have indicated a global reversal of near-surface wind speeds from a declining trend to an increasing trend around 2010;however,it remains unclear whether upper-air wind speeds exhibit a similar reversal.This study evaluates reanalysis products using surface and radiosonde observations to analyze upper-air wind speed variations in the Northern Hemisphere,focusing on their seasonal and latitudinal differences.Results demonstrate that JRA-55 effectively captures wind speed variations in the Northern Hemisphere.Notably,upper-air wind speeds over land experienced a reversal in winter 2010 with significant latitudinal differences.The trend reversal of upper wind speed between the midlatitudes and subtropics presents a dipole pattern.From 1990 to 2010,upper-air wind speeds in the midlatitudes(40°-70°N)significantly declined,while the subtropical zone(20°-40°N)displayed an opposite trend.However,during 2010-2020,wind speeds in the midlatitudes shifted to a significant positive trend,whereas the subtropics experienced a significant negative trend.The variations in Northern Hemisphere winter wind speeds can be attributed to changes in low-level baroclinicity driven by tropical diabatic heating and midlatitude transient eddy feedback.Enhanced diabatic heating and weakened eddy feedback during 1990-2010 contributed to reduced wind speeds in the midlatitudes and increased speeds in the subtropics,while reduced diabatic heating and strengthened eddy feedback during 2010-2020 resulted in increased wind speeds in the midlatitudes and decreased speeds in the subtropics.The reversal of upper-air wind speeds could affect surface wind speeds by downward momentum transfer,which could contribute to the reversal of surface wind speeds.
基金jointly supported by the National Natural Science Foundation of China(Grant Nos.42122034,42075043,42330609)the Second Tibetan Plateau Scientific Expedition and Research program(2019QZKK0103)+2 种基金Key Talent Project in Gansu and Central Guidance Fund for Local Science and Technology Development Projects in Gansu(No.24ZYQA031)the Youth Innovation Promotion Association of Chinese Academy of Sciences(2021427)West Light Foundation of the Chinese Academy of Sciences(xbzg-zdsys-202215)。
文摘Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.
