To curb the worsening tropospheric ozone(O_(3))pollution problem in China,a rapid and accurate identification of O_(3)-precursor sensitivity(OPS)is a crucial prerequisite for formulating effective contingency O_(3) po...To curb the worsening tropospheric ozone(O_(3))pollution problem in China,a rapid and accurate identification of O_(3)-precursor sensitivity(OPS)is a crucial prerequisite for formulating effective contingency O_(3) pollution control strategies.However,currently widely-used methods,such as statistical models and numerical models,exhibit inherent limitations in identifying OPS in a timely and accurate manner.In this study,we developed a novel approach to identify OPS based on eXtreme Gradient Boosting model,Shapley additive explanation(SHAP)al-gorithm,and volatile organic compound(VOC)photochemical decay adjustment,using the meteorology and speciated pollutant monitoring data as the input.By comparing the difference in SHAP values between base sce-nario and precursor reduction scenario for nitrogen oxides(NO_(x))and VOCs,OPS was divided into NO_(x)-limited,VOCs-limited and transition regime.Using the long-lasting O_(3) pollution episode in the autumn of 2022 at the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)as an example,we demonstrated large spatiotemporal heterogeneities of OPS over the GBA,which were generally shifted from NO_(x)-limited to VOCs-limited from September to October and more inclined to be VOCs-limited at the central and NO_(x)-limited in the peripheral areas.This study developed an innovative OPS identification method by comparing the difference in SHAP value before and after precursor emission reduction.Our method enables the accurate identification of OPS in the time scale of seconds,thereby providing a state-of-the-art tool for the rapid guidance of spatial-specific O_(3) control strategies.展开更多
High spatiotemporal resolution infrared radiances from FY-4A/AGRI(Advanced Geostationary Radiation Imager)can provide crucial information for rapidly developing severe convective weather.This study established a symme...High spatiotemporal resolution infrared radiances from FY-4A/AGRI(Advanced Geostationary Radiation Imager)can provide crucial information for rapidly developing severe convective weather.This study established a symmetric observation error model that differentiates between land and sea for FY-4A/AGRI all-sky assimilation,developed an all-sky assimilation scheme for FY-4A/AGRI based on hydrometeor control variables,and investigated the impacts of all-sky FY-4A/AGRI water vapor channels at different altitudes and rapid-update assimilation at different frequencies on the assimilation and forecasting of a severe convective weather event.Results show that simultaneous assimilation of two water vapor channels can enhance precipitation forecasts compared to single-channel assimilation,which is mainly attributable to a more accurate analysis of water vapor and hydrometeor information.Experiments with different assimilation frequencies demonstrate that the hourly assimilation frequency,compared to other frequencies,incorporates the high-frequency information from AGRI while reducing the impact of spurious oscillations caused by excessively high-frequency assimilation.This hourly assimilation frequency reduces the incoordination among thermal,dynamical,and water vapor conditions caused by excessively fast or slow assimilation frequencies,thus improving the forecast accuracy compared to other frequencies.展开更多
Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiote...Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.展开更多
This study investigates the width of the secondary eyewall(SE)immediately following its formation in tropical cyclones with surface environmental winds aligned and counter-aligned with environmental vertical wind shea...This study investigates the width of the secondary eyewall(SE)immediately following its formation in tropical cyclones with surface environmental winds aligned and counter-aligned with environmental vertical wind shear(VWS),using idealized numerical experiments.Results reveal that the SE develops greater radial extent when surface winds align with VWS compared to counter-aligned conditions.In alignment configurations,shear-enhanced surface winds on the right flank amplify surface enthalpy fluxes,thereby elevating boundary-layer entropy within the downshear outer-core region.Subsequently,more vigorous outer rainbands develop,inducing marked acceleration of tangential winds in the outer core preceding SE formation.The resultant radial expansion of supergradient winds near the boundary-layer top triggers widespread convective activity immediately beyond the inner core.Progressive axisymmetrization of this convective forcing ultimately generates an expansive SE structure.展开更多
Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimila...Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimilation is quite challenging.This is because there is limited knowledge about the cross-component background error covariance(BEC)between LST and atmospheric state variables.This study aims to clarify whether there is a relationship between the error of LST and atmospheric variables,and whether this relationship varies spatially and temporally.To this end,the BEC coupled with atmospheric variables and LST was constructed(LST-BEC),and its characteristics were analyzed based on the 2023 mei-yu season.The general characteristics of LST-BEC show that the LST is mainly correlated with the atmospheric temperature and the correlation decreases gradually with a rise in atmospheric height,and the error standard deviation of the LST is noticeably larger than that of the low-level atmospheric temperature.The spatiotemporal characteristics of LST-BEC on the heavy-rain day and light-rain day show that the error correlation and error standard deviation of LST and low-level atmospheric temperature and humidity are closely related to the weather background,and also have obvious diurnal variations.These results provide valuable information for strongly coupled land-atmosphere assimilation.展开更多
A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study em...A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.展开更多
During the boreal summer,intraseasonal oscillations exhibit significant interannual variations in intensity over two key regions:the central-western equatorial Pacific(5°S-5°N,150°E-150°W)and the s...During the boreal summer,intraseasonal oscillations exhibit significant interannual variations in intensity over two key regions:the central-western equatorial Pacific(5°S-5°N,150°E-150°W)and the subtropical Northwestern Pacific(10°-20°N,130°E-175°W).The former is well-documented and considered to be influenced by the ENSO,while the latter has received comparatively less attention and is likely influenced by the Pacific Meridional Mode(PMM),as suggested by partial correlation analysis results.To elucidate the physical processes responsible for the enhanced(weakened)intraseasonal convection over the subtropical northwestern Pacific during warm(cold)PMM years,the authors employed a moisture budget analysis.The findings reveal that during warm PMM years,there is an increase in summer-mean moisture over the subtropical northwestern Pacific.This increase interacts with intensified vertical motion perturbations in the region,leading to greater vertical moisture advection in the lower troposphere and consequently resulting in convective instability.Such a process is pivotal in amplifying intraseasonal convection anomalies.The observational findings were further verified by model experiments forced by PMM-like sea surface temperature patterns.展开更多
Due to the high elevation and cold climate of the Tibetan Plateau,the western region retains extensive snow cover during the summer,which can exhibit rapid variability over the course of just a few days.This study uti...Due to the high elevation and cold climate of the Tibetan Plateau,the western region retains extensive snow cover during the summer,which can exhibit rapid variability over the course of just a few days.This study utilizes numerical experiments to investigate the atmospheric response to extreme Tibetan Plateau snow cover(TPSC)events on a subseasonal timescale during summer.The results indicate that the subseasonal variations in TPSC exert limited impact on nonlocal atmospheric circulation and temperature during this period.Nevertheless,local surface energy and atmospheric temperature exhibit rapid cooling responses to increased snow cover.Specifically,an increase in snow cover over the western Tibetan Plateau leads to a sharp rise in surface albedo,resulting in a reduction in land surface energy and a negative response in the diabatic heating rate from the surface to 350 hPa locally.This negative diabatic heating response subsequently causes a decline in both surface and overlying atmospheric temperatures.The temperature response is confined to the western Tibetan Plateau and extends vertically from the surface to approximately 350 hPa.These extreme TPSC events and their associated atmospheric impacts occur within a two-week timescale.展开更多
Persistent(5-day or longer)extreme cold events(ECEs)over northeastern China during the boreal winter of 1979–2020 are investigated using daily minimum temperature(Tmin)from the China Meteorological Data Network.The e...Persistent(5-day or longer)extreme cold events(ECEs)over northeastern China during the boreal winter of 1979–2020 are investigated using daily minimum temperature(Tmin)from the China Meteorological Data Network.The extreme cooling area and intensity indices associated with the ECEs exhibit a dominant 10–40-day periodicity,indicating a close link with atmospheric intraseasonal oscillations(ISOs).The ECEs are categorized into W-and N-type.In the former,the low-frequency cooling associated with the ISO first penetrates into the western boundary of the northeastern China domain and later occupies the entire domain at its peak phase.The upper-tropospheric circulation associated with this type is characterized by a northwest–southeast-oriented Rossby wave train,expanding from the Ural Mountains to the western Pacific Ocean.In the latter,the cooling invades the northern boundary first and then penetrates into the entire domain.The upper tropospheric precursory signal associated with this type is a zonally oriented negative geopotential height anomaly,which slowly moves southward.A downward-propagating signal is observed in the stratospheric potential vorticity field prior to the peak cooling,implying a possible stratospheric impact.In addition to the W-and N-types,ECEs can also occur in a localized region near either at the northern or southern boundary of the domain.展开更多
Tropospheric ozone pollution has been worsened over most regions of China,adversely affecting human health and ecosystems.The long-term ozone concentration depends largely upon atmospheric circulations.Here,we conduct...Tropospheric ozone pollution has been worsened over most regions of China,adversely affecting human health and ecosystems.The long-term ozone concentration depends largely upon atmospheric circulations.Here,we conducted meteorological adjustment to quantitatively assess the influences of meteorological factors on the ozone evolution in China's seven city clusters during thewarm season(April to October)from 2013 to 2020.Our analysis indicated that northern and eastern regions experienced ozone increases driven by emission changes.Southern regions,particularly the Pearl River Delta(PRD),exhibited ozone rise primarily due to meteorological conditions despite emission changes.In the Sichuan Basin(SCB)and Central Yangtze River Plain(CYP),where ozone levels decreased,meteorological conditions played a significant role in suppressing the ascent of ozone.Empirical orthogonal functions(EOF)analyses suggested that the spatiotemporal trend ofmeteorologyassociated ozone was strongly correlated with the variation of East Asian Trough(EAT),South Asian High(SAH)and the western Pacific subtropical high(WPSH).The top three EOF patterns explained 33.4%,21.8%,and 16.0%of the total variance inmeteorology-associated ozone.Absolute principal component scores-multiple linear regression(APCS-MLR)analyse quantitatively identified that enhanced EAT and SAH with a northward location of WPSH were favourable to surface ozone formation in central and eastern regions,but unfavourable to ozone formation in edge regions such as SCB.展开更多
The discrepancy in the trends of the global zonal mean(GZM)intensity of the Hadley circulation(HCI)between reanalysis data and model simulations has been a problem for understanding the changes in HCI and the influenc...The discrepancy in the trends of the global zonal mean(GZM)intensity of the Hadley circulation(HCI)between reanalysis data and model simulations has been a problem for understanding the changes in HCI and the influence of external forcings.To understand the reason for this discrepancy,this study investigates the trends of intensity of regional HCI of the Northern Hemisphere over the eastern Pacific(EPA),western Pacific(WPA),Atlantic(ATL),Africa(AFR),the Indian Ocean(IDO),and residual area(RA),based on six reanalysis datasets and 13 CMIP6 models.In reanalysis data,the trends in regional HCI over EPA and ATL(WPA and AFR)contribute to(partially offset)the increasing trend in GZM HCI,while the trends in regional HCI over IDO are different in different reanalysis data.The CMIP6 models skillfully reproduce the trends in regional HCI over EPA,ATL,WPA,and AFR,but simulate notable decreasing trends in regional HCI over IDO,which is a key reason for the opposite trends in GZM HCI between reanalysis data and models.The discrepancy in IDO can be attributed to differences in the simulation of diabatic heating and zonal friction between reanalysis data and models.Optimal fingerprint analysis indicates that anthropogenic(ANT)and non-greenhouse gas(NOGHG)forcings are the dominant drivers of the HCI trends in the EPA and ATL regions.In the WPA(AFR)region,NOGHG(ANT)forcing serves as the primary driver.The findings contribute to improving the representation of regional HCI trends in models and improving the attribution of external forcings.展开更多
In 2021,Cempaka,a tiny tropical cyclone,made landfall in China.As the TC intensified prior to landfall,the tropical cyclone size measured with precipitation decreased significantly.A numerical simulation was conducted...In 2021,Cempaka,a tiny tropical cyclone,made landfall in China.As the TC intensified prior to landfall,the tropical cyclone size measured with precipitation decreased significantly.A numerical simulation was conducted to examine the possible processes modulating the storm size.Azimuthally mean potential vorticity(PV)was found to decrease mainly in the middle to upper troposphere between 50-and 80-km radii.The PV budget results indicate that the advection and generation of mean PV associated with asymmetric processes,rather than the symmetric processes,primarily contributed to the decrease in mean PV.These asymmetric processes leading to a negative PV tendency were likely associated with inactive outer rainbands.In contrast,the tangential winds simultaneously expanded radially outward,possibly related to inner-core diabatic heating.The findings here emphasize the importance of outer rainband activity in tropical cyclone size change.展开更多
Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and...Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and local impacts of soil heat capacities on vegetation growth through pathways of atmospheric circulation and land–atmosphere interaction,this paper develops a statistical prediction model for NPP from April to June(AMJ)across the middle-to-high latitudes of Eurasia.The model introduces two physically meaningful predictors:the snow water equivalent(SWE)from February to March(FM)over central Europe and the FM local soil temperature(ST).The positive phase of FM SWE triggers anomalous eastward-propagating Rossby waves,leading to an anomalous low-pressure system and cooling in the middle-to-high latitudes of Eurasia.This effect persists into spring through snow feedback to the atmosphere and affects subsequent NPP changes.The ST is closely related to the AMJ temperature and precipitation.With positive ST anomalies,the AMJ temperature and precipitation exhibit an east–west dipole anomaly distribution in this region.The single-factor prediction scheme using ST as the predictor is much better than using SWE as the predictor.Independent validation results from 2009 to 2014 demonstrate that the ST scheme alone has good predictive performance for the spatial distribution and interannual variability of NPP.The predictive skills of the multi-factor prediction schemes can be improved by about 13%if the ST predictor is included.The findings confirm that local ST is a predictor that must be included for NPP prediction.展开更多
Land–atmosphere coupling and sea surface temperature(SST)anomalies both have essential impacts on weather and climate extremes.Based on the ERA5 reanalysis dataset and the CESM1.2.2 model,this study investigates the ...Land–atmosphere coupling and sea surface temperature(SST)anomalies both have essential impacts on weather and climate extremes.Based on the ERA5 reanalysis dataset and the CESM1.2.2 model,this study investigates the influence of land–atmosphere coupling on summer extreme hot-humid events(EHHE)over southern Eurasia under different SST backgrounds.The results suggest that coupling causes near-surface air temperature increases that exceed 0.5℃.From 1961 to 2020,the frequency of EHHE has continuously increased,and is closely related to soil moisture anomalies in the northern Indian Peninsula(IDP)and the middle and lower reaches of the Yangtze River(YRB).Numerical simulations further demonstrate that land–atmosphere coupling raises the risk of EHHE by 25.4%.In a typical El Niño SST background state,intensified land–atmosphere coupling tends to produce notable increases in the frequency of EHHE.The dominant processes that land–atmosphere coupling affects the EHHE variations are evidently different between these two regions.Land surface thermal anomalies predominate in the IDP,while moisture conditions are more critical in the YRB.When warm SST anomalies exist,dry soil anomalies in the IDP are prominent,and evaporation is constrained,increasing sensible heat flux.Positive geopotential height anomalies are significant,combined with adiabatic warming induced by descending motion and a noticeable warm center in the near-surface atmosphere.The southward shift of the westerly jet enhances divergence over YRB.The anticyclonic circulation anomalies over the western Pacific are conducive to guiding moisture transport to the YRB,providing a favorable circulation background for the development of summer EHHE.展开更多
A numerical simulation was performed using the Thompson microphysics scheme to preliminarily investigate the features of the microphysical processes involved in the record-breaking rainfall event that occurred in Hena...A numerical simulation was performed using the Thompson microphysics scheme to preliminarily investigate the features of the microphysical processes involved in the record-breaking rainfall event that occurred in Henan Province,China,on 20 July 2021.The simulation results showed that a strong meso-𝛾-scale vortical updraft was concurrent with the torrential rainfall.The main finding is that this event was characterized by typical midlatitude warm-rain processes.The simulation with the Thompson microphysics scheme further indicated that highly efficient collision-coalescence of cloud water to rainwater resulted in a considerably active rain droplet growth,leading to this record-breaking rainfall event.展开更多
Deep learning(DL)has become a crucial technique for predicting the El Niño-Southern Oscillation(ENSO)and evaluating its predictability.While various DL-based models have been developed for ENSO predictions,many f...Deep learning(DL)has become a crucial technique for predicting the El Niño-Southern Oscillation(ENSO)and evaluating its predictability.While various DL-based models have been developed for ENSO predictions,many fail to capture the coherent multivariate evolution within the coupled ocean-atmosphere system of the tropical Pacific.To address this three-dimensional(3D)limitation and represent ENSO-related ocean-atmosphere interactions more accurately,a novel this 3D multivariate prediction model was proposed based on a Transformer architecture,which incorporates a spatiotemporal self-attention mechanism.This model,named 3D-Geoformer,offers several advantages,enabling accurate ENSO predictions up to one and a half years in advance.Furthermore,an integrated gradient method was introduced into the model to identify the sources of predictability for sea surface temperature(SST)variability in the eastern equatorial Pacific.Results reveal that the 3D-Geoformer effectively captures ENSO-related precursors during the evolution of ENSO events,particularly the thermocline feedback processes and ocean temperature anomaly pathways on and off the equator.By extending DL-based ENSO predictions from one-dimensional Niño time series to 3D multivariate fields,the 3D-Geoformer represents a significant advancement in ENSO prediction.This study provides details in the model formulation,analysis procedures,sensitivity experiments,and illustrative examples,offering practical guidance for the application of the model in ENSO research.展开更多
This study explores the impact of winter sea surface temperature(SST)anomalies in the Southern Indian Ocean on summer precipitation patterns in China,utilizing data from reanalysis sources and Coupled Model Intercompa...This study explores the impact of winter sea surface temperature(SST)anomalies in the Southern Indian Ocean on summer precipitation patterns in China,utilizing data from reanalysis sources and Coupled Model Intercomparison Project Phase 6(CMIP6)models.The results reveal that the Southern Indian Ocean Dipole(SIOD),characterized by contrasting SST anomalies in the northeast and southwest regions,acts as a predictor for Chinese summer precipitation patterns,namely floods in the south and drought in the north.In a positive SIOD event,the southwestern Indian Ocean exhibits warmer SSTs,while the northeastern region remains cooler.A negative SIOD event shows the opposite pattern.During the positive phase of the SIOD,the winter SST distribution strengthens the 850-hPa cross-equatorial airflow,generating a robust low-level westerly jet that enhances water vapor transport to the Bay of Bengal(BoB).These air-sea interactions maintain lower SSTs in the northeastern region,which significantly increase the land-sea temperature contrast in the Northern Hemisphere during spring and summer.This strengthened thermal gradient intensifies the southwest monsoon,establishing a strong convergence zone near the South China Sea and amplifying monsoon-driven precipitation in South China.Additionally,CMIP6 models,such as NorESM2-LM and NorCPM1,which accurately simulate the SIOD pattern,effectively capture the seasonal response of cross-equatorial airflow driven by SST anomalies of Southern Indian Ocean.The result highlights the essential role of cross-equatorial airflow generated by the SIOD in forecasting crossseasonal precipitation patterns.展开更多
As a prominent mode of variability in the tropical stratosphere on the interannual timescale,the Quasi-Biennial Oscillation(QBO)can significantly influence global atmospheric circulation and weather patterns.This stud...As a prominent mode of variability in the tropical stratosphere on the interannual timescale,the Quasi-Biennial Oscillation(QBO)can significantly influence global atmospheric circulation and weather patterns.This study explores the dynamic processes of QBO disruptions using the integrated climate model of the China Meteorological Administration(CMA)by nudging the tropical zonal winds toward observations.A comparative analysis with ERA5 reanalysis data shows that the nudged runs accurately replicate the general characteristics of the QBO,including the alternating QBO wind regimes and QBO disruption events.The evolution of the QBO winds is diagnosed using empirical orthogonal function and root-mean-square difference analyses,and the rarity of the disruption events is confirmed in the CMA model.Different aspects of the QBO disruptions and the relevant dynamics are present in the model.Firstly,the momentum budget analysis highlights the crucial roles of extratropical Rossby waves and non-orographic gravity waves in the transition from westerly to easterly winds during a disruption.Secondly,Kelvin waves and non-orographic gravity waves explain much of the transition from easterly to westerly winds near 40 hPa.Thirdly,the positive tendency from enhanced vertical advection further accelerates westerly momentum development via secondary meridional circulation.These findings underscore the importance of nudging techniques in understanding QBO dynamics,which provides valuable insights for future climate model improvements toward better forecasting QBO-related climate variability.Notably,due to model limitations,no QBO disruptions were simulated in the free-run experiments.展开更多
基金supported by the Key-Area Research and Development Program of Guangdong Province(No.2020B1111360003)the National Natural Science Foundation of China(Nos.42465008 and 42105164)+2 种基金Yunnan Science and Technology Department Project(No.202501AT070239)Yunnan Science and Technology Department Youth Project(No.202401AU070202)Xianyang Rapid Response Decision Support Project for Ozone(No.YZ2024-ZB019).
文摘To curb the worsening tropospheric ozone(O_(3))pollution problem in China,a rapid and accurate identification of O_(3)-precursor sensitivity(OPS)is a crucial prerequisite for formulating effective contingency O_(3) pollution control strategies.However,currently widely-used methods,such as statistical models and numerical models,exhibit inherent limitations in identifying OPS in a timely and accurate manner.In this study,we developed a novel approach to identify OPS based on eXtreme Gradient Boosting model,Shapley additive explanation(SHAP)al-gorithm,and volatile organic compound(VOC)photochemical decay adjustment,using the meteorology and speciated pollutant monitoring data as the input.By comparing the difference in SHAP values between base sce-nario and precursor reduction scenario for nitrogen oxides(NO_(x))and VOCs,OPS was divided into NO_(x)-limited,VOCs-limited and transition regime.Using the long-lasting O_(3) pollution episode in the autumn of 2022 at the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)as an example,we demonstrated large spatiotemporal heterogeneities of OPS over the GBA,which were generally shifted from NO_(x)-limited to VOCs-limited from September to October and more inclined to be VOCs-limited at the central and NO_(x)-limited in the peripheral areas.This study developed an innovative OPS identification method by comparing the difference in SHAP value before and after precursor emission reduction.Our method enables the accurate identification of OPS in the time scale of seconds,thereby providing a state-of-the-art tool for the rapid guidance of spatial-specific O_(3) control strategies.
基金supported by the National Key R&D Program of China(Grant No.2022YFC3080500)the National Natural Science Foundation of China(Grant Nos.U2142208,42475158,and 42105149)the High-Performance Computing Center of Nanjing University of Information Science&Technology for supporting this work。
文摘High spatiotemporal resolution infrared radiances from FY-4A/AGRI(Advanced Geostationary Radiation Imager)can provide crucial information for rapidly developing severe convective weather.This study established a symmetric observation error model that differentiates between land and sea for FY-4A/AGRI all-sky assimilation,developed an all-sky assimilation scheme for FY-4A/AGRI based on hydrometeor control variables,and investigated the impacts of all-sky FY-4A/AGRI water vapor channels at different altitudes and rapid-update assimilation at different frequencies on the assimilation and forecasting of a severe convective weather event.Results show that simultaneous assimilation of two water vapor channels can enhance precipitation forecasts compared to single-channel assimilation,which is mainly attributable to a more accurate analysis of water vapor and hydrometeor information.Experiments with different assimilation frequencies demonstrate that the hourly assimilation frequency,compared to other frequencies,incorporates the high-frequency information from AGRI while reducing the impact of spurious oscillations caused by excessively high-frequency assimilation.This hourly assimilation frequency reduces the incoordination among thermal,dynamical,and water vapor conditions caused by excessively fast or slow assimilation frequencies,thus improving the forecast accuracy compared to other frequencies.
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]an Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]+3 种基金a project supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209]a Research Council of Norway funded project(MAPARC)[grant number 328943]a Nansen Center´s basic institutional funding[grant number 342624]the high-performance computing support from the School of Atmospheric Science at Sun Yat-sen University。
文摘Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.
基金jointly supported by the National Natural Science Foundation of China[grant numbers U2342202,42175005,and 42175016]the Qing Lan Project[grant number R2023Q06]。
文摘This study investigates the width of the secondary eyewall(SE)immediately following its formation in tropical cyclones with surface environmental winds aligned and counter-aligned with environmental vertical wind shear(VWS),using idealized numerical experiments.Results reveal that the SE develops greater radial extent when surface winds align with VWS compared to counter-aligned conditions.In alignment configurations,shear-enhanced surface winds on the right flank amplify surface enthalpy fluxes,thereby elevating boundary-layer entropy within the downshear outer-core region.Subsequently,more vigorous outer rainbands develop,inducing marked acceleration of tangential winds in the outer core preceding SE formation.The resultant radial expansion of supergradient winds near the boundary-layer top triggers widespread convective activity immediately beyond the inner core.Progressive axisymmetrization of this convective forcing ultimately generates an expansive SE structure.
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
基金sponsored by the National Natural Science Foundation of China[grant number U2442218]。
文摘Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimilation is quite challenging.This is because there is limited knowledge about the cross-component background error covariance(BEC)between LST and atmospheric state variables.This study aims to clarify whether there is a relationship between the error of LST and atmospheric variables,and whether this relationship varies spatially and temporally.To this end,the BEC coupled with atmospheric variables and LST was constructed(LST-BEC),and its characteristics were analyzed based on the 2023 mei-yu season.The general characteristics of LST-BEC show that the LST is mainly correlated with the atmospheric temperature and the correlation decreases gradually with a rise in atmospheric height,and the error standard deviation of the LST is noticeably larger than that of the low-level atmospheric temperature.The spatiotemporal characteristics of LST-BEC on the heavy-rain day and light-rain day show that the error correlation and error standard deviation of LST and low-level atmospheric temperature and humidity are closely related to the weather background,and also have obvious diurnal variations.These results provide valuable information for strongly coupled land-atmosphere assimilation.
基金supported by the National Natural Science Foundation of China [grant number 42030605]the National Key R&D Program of China [grant number 2020YFA0608004]。
文摘A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.
基金supported by the National Natural Science Foundation of China [grant number 42088101]。
文摘During the boreal summer,intraseasonal oscillations exhibit significant interannual variations in intensity over two key regions:the central-western equatorial Pacific(5°S-5°N,150°E-150°W)and the subtropical Northwestern Pacific(10°-20°N,130°E-175°W).The former is well-documented and considered to be influenced by the ENSO,while the latter has received comparatively less attention and is likely influenced by the Pacific Meridional Mode(PMM),as suggested by partial correlation analysis results.To elucidate the physical processes responsible for the enhanced(weakened)intraseasonal convection over the subtropical northwestern Pacific during warm(cold)PMM years,the authors employed a moisture budget analysis.The findings reveal that during warm PMM years,there is an increase in summer-mean moisture over the subtropical northwestern Pacific.This increase interacts with intensified vertical motion perturbations in the region,leading to greater vertical moisture advection in the lower troposphere and consequently resulting in convective instability.Such a process is pivotal in amplifying intraseasonal convection anomalies.The observational findings were further verified by model experiments forced by PMM-like sea surface temperature patterns.
基金supported by the National Key R&D Program of China[grant number 2022YFF0801603]the Natural Science Foundation of China[grant number 41905074].
文摘Due to the high elevation and cold climate of the Tibetan Plateau,the western region retains extensive snow cover during the summer,which can exhibit rapid variability over the course of just a few days.This study utilizes numerical experiments to investigate the atmospheric response to extreme Tibetan Plateau snow cover(TPSC)events on a subseasonal timescale during summer.The results indicate that the subseasonal variations in TPSC exert limited impact on nonlocal atmospheric circulation and temperature during this period.Nevertheless,local surface energy and atmospheric temperature exhibit rapid cooling responses to increased snow cover.Specifically,an increase in snow cover over the western Tibetan Plateau leads to a sharp rise in surface albedo,resulting in a reduction in land surface energy and a negative response in the diabatic heating rate from the surface to 350 hPa locally.This negative diabatic heating response subsequently causes a decline in both surface and overlying atmospheric temperatures.The temperature response is confined to the western Tibetan Plateau and extends vertically from the surface to approximately 350 hPa.These extreme TPSC events and their associated atmospheric impacts occur within a two-week timescale.
基金supported by the National Natural Science Foundation of China(Grant Nos.42088101 and 42075032).
文摘Persistent(5-day or longer)extreme cold events(ECEs)over northeastern China during the boreal winter of 1979–2020 are investigated using daily minimum temperature(Tmin)from the China Meteorological Data Network.The extreme cooling area and intensity indices associated with the ECEs exhibit a dominant 10–40-day periodicity,indicating a close link with atmospheric intraseasonal oscillations(ISOs).The ECEs are categorized into W-and N-type.In the former,the low-frequency cooling associated with the ISO first penetrates into the western boundary of the northeastern China domain and later occupies the entire domain at its peak phase.The upper-tropospheric circulation associated with this type is characterized by a northwest–southeast-oriented Rossby wave train,expanding from the Ural Mountains to the western Pacific Ocean.In the latter,the cooling invades the northern boundary first and then penetrates into the entire domain.The upper tropospheric precursory signal associated with this type is a zonally oriented negative geopotential height anomaly,which slowly moves southward.A downward-propagating signal is observed in the stratospheric potential vorticity field prior to the peak cooling,implying a possible stratospheric impact.In addition to the W-and N-types,ECEs can also occur in a localized region near either at the northern or southern boundary of the domain.
基金supported by the National Natural Science Foundation of China(No.42377095)the Open Research Fund Program of Plateau Atmosphere and Environment Key Laboratory of Sichuan Province(No.PAEKL-2024-K01)Xianyang Key Research and Development Program(No.L2022ZDYFSF040).
文摘Tropospheric ozone pollution has been worsened over most regions of China,adversely affecting human health and ecosystems.The long-term ozone concentration depends largely upon atmospheric circulations.Here,we conducted meteorological adjustment to quantitatively assess the influences of meteorological factors on the ozone evolution in China's seven city clusters during thewarm season(April to October)from 2013 to 2020.Our analysis indicated that northern and eastern regions experienced ozone increases driven by emission changes.Southern regions,particularly the Pearl River Delta(PRD),exhibited ozone rise primarily due to meteorological conditions despite emission changes.In the Sichuan Basin(SCB)and Central Yangtze River Plain(CYP),where ozone levels decreased,meteorological conditions played a significant role in suppressing the ascent of ozone.Empirical orthogonal functions(EOF)analyses suggested that the spatiotemporal trend ofmeteorologyassociated ozone was strongly correlated with the variation of East Asian Trough(EAT),South Asian High(SAH)and the western Pacific subtropical high(WPSH).The top three EOF patterns explained 33.4%,21.8%,and 16.0%of the total variance inmeteorology-associated ozone.Absolute principal component scores-multiple linear regression(APCS-MLR)analyse quantitatively identified that enhanced EAT and SAH with a northward location of WPSH were favourable to surface ozone formation in central and eastern regions,but unfavourable to ozone formation in edge regions such as SCB.
基金the National Key Research and Development Program of China[grant number 2022YFF0801704].
文摘The discrepancy in the trends of the global zonal mean(GZM)intensity of the Hadley circulation(HCI)between reanalysis data and model simulations has been a problem for understanding the changes in HCI and the influence of external forcings.To understand the reason for this discrepancy,this study investigates the trends of intensity of regional HCI of the Northern Hemisphere over the eastern Pacific(EPA),western Pacific(WPA),Atlantic(ATL),Africa(AFR),the Indian Ocean(IDO),and residual area(RA),based on six reanalysis datasets and 13 CMIP6 models.In reanalysis data,the trends in regional HCI over EPA and ATL(WPA and AFR)contribute to(partially offset)the increasing trend in GZM HCI,while the trends in regional HCI over IDO are different in different reanalysis data.The CMIP6 models skillfully reproduce the trends in regional HCI over EPA,ATL,WPA,and AFR,but simulate notable decreasing trends in regional HCI over IDO,which is a key reason for the opposite trends in GZM HCI between reanalysis data and models.The discrepancy in IDO can be attributed to differences in the simulation of diabatic heating and zonal friction between reanalysis data and models.Optimal fingerprint analysis indicates that anthropogenic(ANT)and non-greenhouse gas(NOGHG)forcings are the dominant drivers of the HCI trends in the EPA and ATL regions.In the WPA(AFR)region,NOGHG(ANT)forcing serves as the primary driver.The findings contribute to improving the representation of regional HCI trends in models and improving the attribution of external forcings.
基金jointly supported by the National Natural Science Foundation of China[grant numbers U2342202 and 42175005]the Qing Lan Project[grant number R2023Q06]。
文摘In 2021,Cempaka,a tiny tropical cyclone,made landfall in China.As the TC intensified prior to landfall,the tropical cyclone size measured with precipitation decreased significantly.A numerical simulation was conducted to examine the possible processes modulating the storm size.Azimuthally mean potential vorticity(PV)was found to decrease mainly in the middle to upper troposphere between 50-and 80-km radii.The PV budget results indicate that the advection and generation of mean PV associated with asymmetric processes,rather than the symmetric processes,primarily contributed to the decrease in mean PV.These asymmetric processes leading to a negative PV tendency were likely associated with inactive outer rainbands.In contrast,the tangential winds simultaneously expanded radially outward,possibly related to inner-core diabatic heating.The findings here emphasize the importance of outer rainband activity in tropical cyclone size change.
基金funded by the National Natural Science Foundation of China[grant numbers 42075115 and 41991285]the Joint Open Project of KLME&CIC-FEMD[grant number KLME201901]。
文摘Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and local impacts of soil heat capacities on vegetation growth through pathways of atmospheric circulation and land–atmosphere interaction,this paper develops a statistical prediction model for NPP from April to June(AMJ)across the middle-to-high latitudes of Eurasia.The model introduces two physically meaningful predictors:the snow water equivalent(SWE)from February to March(FM)over central Europe and the FM local soil temperature(ST).The positive phase of FM SWE triggers anomalous eastward-propagating Rossby waves,leading to an anomalous low-pressure system and cooling in the middle-to-high latitudes of Eurasia.This effect persists into spring through snow feedback to the atmosphere and affects subsequent NPP changes.The ST is closely related to the AMJ temperature and precipitation.With positive ST anomalies,the AMJ temperature and precipitation exhibit an east–west dipole anomaly distribution in this region.The single-factor prediction scheme using ST as the predictor is much better than using SWE as the predictor.Independent validation results from 2009 to 2014 demonstrate that the ST scheme alone has good predictive performance for the spatial distribution and interannual variability of NPP.The predictive skills of the multi-factor prediction schemes can be improved by about 13%if the ST predictor is included.The findings confirm that local ST is a predictor that must be included for NPP prediction.
基金supported by the National Science Foundation of China(Grant Nos.42088101 and 42275172).
文摘Land–atmosphere coupling and sea surface temperature(SST)anomalies both have essential impacts on weather and climate extremes.Based on the ERA5 reanalysis dataset and the CESM1.2.2 model,this study investigates the influence of land–atmosphere coupling on summer extreme hot-humid events(EHHE)over southern Eurasia under different SST backgrounds.The results suggest that coupling causes near-surface air temperature increases that exceed 0.5℃.From 1961 to 2020,the frequency of EHHE has continuously increased,and is closely related to soil moisture anomalies in the northern Indian Peninsula(IDP)and the middle and lower reaches of the Yangtze River(YRB).Numerical simulations further demonstrate that land–atmosphere coupling raises the risk of EHHE by 25.4%.In a typical El Niño SST background state,intensified land–atmosphere coupling tends to produce notable increases in the frequency of EHHE.The dominant processes that land–atmosphere coupling affects the EHHE variations are evidently different between these two regions.Land surface thermal anomalies predominate in the IDP,while moisture conditions are more critical in the YRB.When warm SST anomalies exist,dry soil anomalies in the IDP are prominent,and evaporation is constrained,increasing sensible heat flux.Positive geopotential height anomalies are significant,combined with adiabatic warming induced by descending motion and a noticeable warm center in the near-surface atmosphere.The southward shift of the westerly jet enhances divergence over YRB.The anticyclonic circulation anomalies over the western Pacific are conducive to guiding moisture transport to the YRB,providing a favorable circulation background for the development of summer EHHE.
基金jointly supported by the National Natural Science Foundation of China[grant numbers U2342202 and 42175005]the Qing Lan Project[grant number R2023Q06]。
文摘A numerical simulation was performed using the Thompson microphysics scheme to preliminarily investigate the features of the microphysical processes involved in the record-breaking rainfall event that occurred in Henan Province,China,on 20 July 2021.The simulation results showed that a strong meso-𝛾-scale vortical updraft was concurrent with the torrential rainfall.The main finding is that this event was characterized by typical midlatitude warm-rain processes.The simulation with the Thompson microphysics scheme further indicated that highly efficient collision-coalescence of cloud water to rainwater resulted in a considerably active rain droplet growth,leading to this record-breaking rainfall event.
基金Supported by the Laoshan Laboratory(No.LSKJ202202402)the National Natural Science Foundation of China(No.42030410)+2 种基金the Startup Foundation for Introducing Talent of Nanjing University of Information Science&Technology,and Jiangsu Innovation Research Group(No.JSSCTD 202346)supported by the China National Postdoctoral Program for Innovative Talents(No.BX20240169)the China Postdoctoral Science Foundation(No.2141062400101)。
文摘Deep learning(DL)has become a crucial technique for predicting the El Niño-Southern Oscillation(ENSO)and evaluating its predictability.While various DL-based models have been developed for ENSO predictions,many fail to capture the coherent multivariate evolution within the coupled ocean-atmosphere system of the tropical Pacific.To address this three-dimensional(3D)limitation and represent ENSO-related ocean-atmosphere interactions more accurately,a novel this 3D multivariate prediction model was proposed based on a Transformer architecture,which incorporates a spatiotemporal self-attention mechanism.This model,named 3D-Geoformer,offers several advantages,enabling accurate ENSO predictions up to one and a half years in advance.Furthermore,an integrated gradient method was introduced into the model to identify the sources of predictability for sea surface temperature(SST)variability in the eastern equatorial Pacific.Results reveal that the 3D-Geoformer effectively captures ENSO-related precursors during the evolution of ENSO events,particularly the thermocline feedback processes and ocean temperature anomaly pathways on and off the equator.By extending DL-based ENSO predictions from one-dimensional Niño time series to 3D multivariate fields,the 3D-Geoformer represents a significant advancement in ENSO prediction.This study provides details in the model formulation,analysis procedures,sensitivity experiments,and illustrative examples,offering practical guidance for the application of the model in ENSO research.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)National Natural Science Foundation of China(U2442202)+1 种基金Key Innovation Team of China Meteorological Administration“Climate Change Detection and Response”(CMA2022ZD03)National Key Research and Development Program of China(2023YFF0805104)。
文摘This study explores the impact of winter sea surface temperature(SST)anomalies in the Southern Indian Ocean on summer precipitation patterns in China,utilizing data from reanalysis sources and Coupled Model Intercomparison Project Phase 6(CMIP6)models.The results reveal that the Southern Indian Ocean Dipole(SIOD),characterized by contrasting SST anomalies in the northeast and southwest regions,acts as a predictor for Chinese summer precipitation patterns,namely floods in the south and drought in the north.In a positive SIOD event,the southwestern Indian Ocean exhibits warmer SSTs,while the northeastern region remains cooler.A negative SIOD event shows the opposite pattern.During the positive phase of the SIOD,the winter SST distribution strengthens the 850-hPa cross-equatorial airflow,generating a robust low-level westerly jet that enhances water vapor transport to the Bay of Bengal(BoB).These air-sea interactions maintain lower SSTs in the northeastern region,which significantly increase the land-sea temperature contrast in the Northern Hemisphere during spring and summer.This strengthened thermal gradient intensifies the southwest monsoon,establishing a strong convergence zone near the South China Sea and amplifying monsoon-driven precipitation in South China.Additionally,CMIP6 models,such as NorESM2-LM and NorCPM1,which accurately simulate the SIOD pattern,effectively capture the seasonal response of cross-equatorial airflow driven by SST anomalies of Southern Indian Ocean.The result highlights the essential role of cross-equatorial airflow generated by the SIOD in forecasting crossseasonal precipitation patterns.
基金funded by the National Natural Science Foundation of China(Grant No.42275056).
文摘As a prominent mode of variability in the tropical stratosphere on the interannual timescale,the Quasi-Biennial Oscillation(QBO)can significantly influence global atmospheric circulation and weather patterns.This study explores the dynamic processes of QBO disruptions using the integrated climate model of the China Meteorological Administration(CMA)by nudging the tropical zonal winds toward observations.A comparative analysis with ERA5 reanalysis data shows that the nudged runs accurately replicate the general characteristics of the QBO,including the alternating QBO wind regimes and QBO disruption events.The evolution of the QBO winds is diagnosed using empirical orthogonal function and root-mean-square difference analyses,and the rarity of the disruption events is confirmed in the CMA model.Different aspects of the QBO disruptions and the relevant dynamics are present in the model.Firstly,the momentum budget analysis highlights the crucial roles of extratropical Rossby waves and non-orographic gravity waves in the transition from westerly to easterly winds during a disruption.Secondly,Kelvin waves and non-orographic gravity waves explain much of the transition from easterly to westerly winds near 40 hPa.Thirdly,the positive tendency from enhanced vertical advection further accelerates westerly momentum development via secondary meridional circulation.These findings underscore the importance of nudging techniques in understanding QBO dynamics,which provides valuable insights for future climate model improvements toward better forecasting QBO-related climate variability.Notably,due to model limitations,no QBO disruptions were simulated in the free-run experiments.
