Subseasonal Prediction of Early-summer Northeast Asian Cut-off Lows by BCC-CSM2-HR and GloSea5

Northeast Asian cut-off lows are crucial cyclonic systems that can bring temperature and precipitation extremes over large areas.Skillful subseasonal forecasting of Northeast Asian cut-off lows is of great importance.Using two dynamical forecasting systems,one from the Beijing Climate Center(BCC-CSM2-HR)and the other from the Met Office(GloSea5),this study assesses simulation ability and subseasonal prediction skill for early-summer Northeast Asian cut-off lows.Both models are shown to have good ability in representing the spatial structure of cut-off lows,but they underestimate the intensity.The skillful prediction time scales for cut-off low intensity are about 10.2 days for BCC-CSM2-HR and 11.4 days for GloSea5 in advance.Further examination shows that both models can essentially capture the initial Rossby wave train,rapid growth and decay processes responsible for the evolution of cut-off lows,but the models show weaker amplitudes for the three-stage processes.The underestimated simulated strength of both the Eurasian midlatitude and East Asian subtropical jets may lead to the weaker local eddy-mean flow interaction responsible for the cut-off low evolution.

Seamless Prediction in China: A Review

Seamless prediction is a weather-climate integrated prediction covering multiple time scales that include days,weeks,months,seasons,years,and decades.Seamless prediction can provide different industries with information such as weather conditions and climate variations from the next few days to years,which have important impacts on economic and social development and important reference value for short-,medium-and long-term decision-making and planning of the country.Therefore,seamless prediction has received widespread attention from the international scientific community recently.As Chinese scientists have also carried out relevant research,this paper reviews the research in China on developments and applications of seamless prediction methods and prediction systems in recent years.Among them,the main progress of seamless prediction methods studies is reviewed from four aspects:short-and medium-range weather forecasting,subseasonal-to-seasonal,seasonal-to-interannual,and decadal climate prediction.In terms of development and application of seamless prediction systems,the main achievements made by meteorological operational departments,scientific institutes,and universities in China in recent years are reviewed.Finally,some of the issues in seamless prediction that need further study are discussed.

Dynamical Predictability of Leading Interannual Variability Modes of the Asian-Australian Monsoon in Climate Models

The dynamical prediction of the Asian-Australian monsoon(AAM)has been an important and long-standing issue in climate science.In this study,the predictability of the first two leading modes of the AAM is studied using retrospective prediction datasets from the seasonal forecasting models in four operational centers worldwide.Results show that the model predictability of the leading AAM modes is sensitive to how they are defined in different seasonal sequences,especially for the second mode.The first AAM mode,from various seasonal sequences,coincides with the El Niño phase transition in the eastern-central Pacific.The second mode,initialized from boreal summer and autumn,leads El Niño by about one year but can exist during the decay phase of El Niño when initialized from boreal winter and spring.Our findings hint that ENSO,as an early signal,is conducive to better performance of model predictions in capturing the spatiotemporal variations of the leading AAM modes.Still,the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial features.The multimodel ensemble(MME)mean shows some advantage in capturing the spatiotemporal variations of the AAM modes but does not provide a significant improvement in predicting its temporal features compared to the best individual models in predicting its temporal features.The BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM modes.The predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services.

Improvement of Soil Moisture Simulation in Eurasia by the Beijing Climate Center Climate System Model from CMIP5 to CMIP6

This study provides a comprehensive evaluation of historical surface soil moisture simulation(1979-2012)over Eurasia at annual and seasonal time scales between two medium-resolution versions of the Beijing Climate Center Climate System Model(BCC-CSM)—one that is currently participating in phase 6 of the Coupled Model Intercomparison Project(CMIP6),i.e.,BCC-CSM2-MR,and the other,BCC-CSM1.1m,which participated in CMIP5.We show that BCC-CSM2-MR is more skillful in reproducing the climate mean states and standard deviations of soil moisture,with pattern correlations increased and biases reduced significantly.BCC-CSM2-MR performs better in capturing the first two primary patterns of soil moisture anomalies,where the period of the corresponding time series is closer to that of reference data.Comparisons show that BCC-CSM2-MR performs at a high level among multiple models of CMIP6 in terms of centered pattern correlation and“amplitude of variation”(relative standard deviation).In general,the centered pattern correlation of BCC-CSM2-MR,ranging from 0.61 to 0.87,is higher than the multi-model mean of CMIP6,and the relative standard deviation is 0.75,which surmounts the overestimations in most of the CMIP6 models.Due to the vital role played by precipitation in land-atmosphere interaction,possible causes of the improvement of soil moisture simulation are further related to precipitation in BCC-CSM2-MR.The results indicate that a better description of the relationship between soil moisture and precipitation and a better reproduction of the climate mean precipitation by the model may result in the improved performance of soil moisture simulation.

Seasonal Forecasts of the Summer 2016 Yangtze River Basin Rainfall

The Yangtze River has been subject to heavy flooding throughout history, and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods. Dams along the river help to manage flood waters, and are important sources of electricity for the region. Being able to forecast high-impact events at long lead times therefore has enormous potential benefit. Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services. The teleconnection from E1 Nifio to Yangtze River basin rainfall meant that the strong E1 Nifio in winter 2015/16 provided a valuable opportunity to test the application of a dynamical forecast system. This paper therefore presents a case study of a real-time seasonal forecast for the Yangtze River basin, building on previous work demonstrating the retrospective skill of such a forecast. A simple forecasting methodology is presented, in which the forecast probabilities are derived from the historical relationship between hindcast and observations. Its performance for 2016 is discussed. The heavy rainfall in the May-June-July period was correctly forecast well in advance. August saw anomalously low rainfall, and the forecasts for the June-July-August period correctly showed closer to average levels. The forecasts contributed to the confidence of decision-makers across the Yangtze River basin. Trials of climate services such as this help to promote appropriate use of seasonal forecasts, and highlight areas for future improvements.

Diagnosing SST Error Growth during ENSO Developing Phase in the BCC_CSM1.1(m) Prediction System

In this study, the predictability of the El Nino-South Oscillation(ENSO) in an operational prediction model from the perspective of initial errors is diagnosed using the seasonal hindcasts of the Beijing Climate Center System Model,BCC;SM1.1(m). Forecast skills during the different ENSO phases are analyzed and it is shown that the ENSO forecasts appear to be more challenging during the developing phase, compared to the decay phase. During ENSO development, the SST prediction errors are significantly negative and cover a large area in the central and eastern tropical Pacific, thus limiting the model skill in predicting the intensity of El Nino. The large-scale SST errors, at their early stage, are generated gradually in terms of negative anomalies in the subsurface ocean temperature over the central-western equatorial Pacific,featuring an error evolutionary process similar to that of El Nino decay and the transition to the La Nina growth phase.Meanwhile, for short lead-time ENSO predictions, the initial wind errors begin to play an increasing role, particularly in linking with the subsurface heat content errors in the central-western Pacific. By comparing the multiple samples of initial fields in the model, it is clearly found that poor SST predictions of the Nino-3.4 region are largely due to contributions of the initial errors in certain specific locations in the tropical Pacific. This demonstrates that those sensitive areas for initial fields in ENSO prediction are fairly consistent in both previous ideal experiments and our operational predictions,indicating the need for targeted observations to further improve operational forecasts of ENSO.

Dynamical Feedback between Synoptic Eddy and Low-Frequency Flow as Simulated by BCC_CSM1.1(m)

Since the interaction between atmospheric synoptic eddy （SE） （2-8 days） activity and low-frequency （LF） （monthly） flow （referred to as SELF） plays an essential role in generating and maintaining dominant climate modes, an evaluation of the performance of BCC_CSMI.I（m） in simulating the SE feedback onto the LF flow is given in this paper. The model captures well the major spatial features of climatological eddy vorticity forcing, eddy-induced growth rate, and patterns of SELF feedback for the climate modes with large magnitudes in cold seasons and small magnitudes in warm seasons for both the Northern and Southern Hemisphere. As in observations, the eddy-induced growth rate and SELF feedback patterns in the model also show positive SE feedback. Overall, the relationships between SE and LF flow show that BCC_CSM1. l（m） satisfactorily captures the basic features of positive SE feedback, which demonstrates the simulation skill of the model for LF variability. Specifically, such an evaluation can help to find model biases of BCC_CSM1.1 （m） in simulating SE feedback, which will provide a reference for the model＇s application.

Indian Ocean SST modes and Their Impacts as Simulated in BCC_CSM1.1(m) and HadGEM3

The sea surface temperature anomalies （SSTAs） in the tropical Indian Ocean （TIO） show two dominant modes at interan- nual time scales, referred to as the Indian Ocean basin mode （IOBM） and dipole mode （IOD）. Recent studies have shown that the IOBM and IOD not only affect the local climate, but also induce remarkable influences in East Asia via teleconnections. In this study, we assess simulations of the IOBM and IOD, as well as their teleconnections, using the operational seasonal pre- diction models from the Met Office （HadGEM3） and Beijing Climate Center [BCC_CSMI.I（m）]. It is demonstrated that the spatial patterns and seasonal cycles axe generally reproduced by the control simulations of BCC_CSM1.1 （m） and HadGEM3, although spectra biases exist. The relationship between the TIO SSTA and E1 Nifio is successfully simulated by both mod- els, including the persistent IOBM warming following E1 Nifio and the IOD-E1 Nifio interactions. BCC_CSMI.I（m） and HadGEM3 axe capable of simulating the observed local impact of the IOBM, such as the strengthening of the South Asian high. The influences of the IOBM on Yangtze River rainfall are also captured well by both models, although this teleconnec- tion is slightly weaker in BCC_CSM 1.1 （m） due to the underestimation of the northwestern Pacific subtropical high. The local effect of the IOD on East African rainfall is reproduced by both models. However, the remote control of the IOD on rainfall over southwestern China is not clear in either model. It is shown that the realistic simulations of TIO SST modes and their teleconnections give rise to the source of skillful seasonal predictions over China.

Verification of Seasonal Prediction by the Upgraded China Multi-Model Ensemble Prediction System (CMMEv2.0)

Based on a combination of six Chinese climate models and three international operational models,the China multimodel ensemble(CMME)prediction system has been upgraded into its version 2(CMMEv2.0)at the National Climate Centre(NCC)of the China Meteorological Administration(CMA)by including new model members and expanding prediction products.A comprehensive assessment of the performance of the upgraded CMME during its hindcast(1993–2016)and real-time prediction(2021–present)periods is conducted in this study.The results demonstrate that CMMEv2.0 outperforms all the individual models by capturing more realistic equatorial sea surface temperature(SST)variability.It exhibits better prediction skills for precipitation and 2-m temperature anomalies,and the improvements in prediction skill of CMMEv2.0 are significant over East Asia.The superiority of CMMEv2.0 can be attributed to its better projection of El Niño–Southern Oscillation(ENSO;with the temporal correlation coefficient score for Niño3.4 index reaching 0.87 at 6-month lead)and ENSO-related teleconnections.As for the real-time prediction in recent three years,CMMEv2.0 has also yielded relatively stable skills;it successfully predicted the primary rainbelt over northern China in summers of 2021–2023 and the warm conditions in winters of 2022/2023.Beyond that,ensemble sampling experiments indicate that the CMMEv2.0 skills become saturated after the ensemble model number increased to 5–6,indicating that selection of only an optimal subgroup of ensemble models could benefit the prediction performance,especially over the extratropics,yet the underlying reasons await future investigation.

Predictability and Risk of Extreme Winter PM_(2.5)Concentration in Beijing

Air pollution remains a serious environmental and social problem in many big cities in the world.How to predict and estimate the risk of extreme air pollution is unsettled yet.This study tries to provide a solution to this challenge by examining the winter PM_(2.5)concentration in Beijing based on the UNprecedented Simulation of Extremes with ENsembles(UNSEEN)method.The PM_(2.5)concentration observations in Beijing,Japanese 55-yr reanalysis data,and the Met Office near term climate prediction system(DePreSys3a)large ensemble simulations are used,and 10,000proxy series are generated with the model fidelity test.It is found that in Beijing,the main meteorological driver of PM_(2.5)concentration is monthly 850-hPa meridional wind(V850).Although the skill in prediction of V850 is low on seasonal and longer timescales,based on the UNSEEN,we use large ensemble of initialized climate simulations of V850 to estimate the current chance and risk of unprecedented PM_(2.5)concentration in Beijing.We unravel that there is a 3%(2.1%–3.9%)chance of unprecedented low monthly V850 corresponding to high PM_(2.5)in each winter,within the 95%range,calculated by bootstrap resampling of the data.Moreover,we use the relationship between air quality and winds to remove the meridional wind influence from the observed record,and find that anthropogenic intervention appears to have reduced the risk of extreme PM_(2.5)in Beijing in recent years.

The China Multi-Model Ensemble Prediction System and Its Application to Flood-Season Prediction in 2018

Multi-model ensemble prediction is an effective approach for improving the prediction skill short-term climate prediction and evaluating related uncertainties. Based on a combination of localized operation outputs of Chinese climate models and imported forecast data of some international operational models, the National Climate Center of the China Meteorological Administration has established the China multi-model ensemble prediction system version 1.0 (CMMEv1.0) for monthly-seasonal prediction of primary climate variability modes and climate elements. We verified the real-time forecasts of CMMEv1.0 for the 2018 flood season (June-August) starting from March 2018 and evaluated the 1991-2016 hindcasts of CMMEv1.0. The results show that CMMEv1.0 has a significantly high prediction skill for global sea surface temperature (SST) anomalies, especially for the El Nino-Southern Oscillation (ENSO) in the tropical central-eastern Pacific. Additionally, its prediction skill for the North Atlantic SST triple (NAST) mode is high, but is relatively low for the Indian Ocean Dipole (IOD) mode. Moreover, CMMEv1.0 has high skills in predicting the western Pacific subtropical high (WPSH) and East Asian summer monsoon (EASM) in the June-July-August (JJA) season. The JJA air temperature in the CMMEv1.0 is predicted with a fairly high skill in most regions of China, while the JJA precipitation exhibits some skills only in northwestern and eastern China. For real-time forecasts in March-August 2018, CMMEv1.0 has accurately predicted the ENSO phase transition from cold to neutral in the tropical central-eastern Pacific and captures evolutions of the NAST and IOD indices in general. The system has also captured the main features of the summer WPSH and EASM indices in 2018, except that the predicted EASM is slightly weaker than the observed. Furthermore, CMMEv1.0 has also successfully predicted warmer air temperatures in northern China and captured the primary rainbelt over northern China, except that it predicted much more precipitation in the middle and lower reaches of the Yangtze River than observation.

Identification Standard for ENSO Events and Its Application to Climate Monitoring and Prediction in China

The El Ni?o–Southern Oscillation(ENSO)reflects anomalous variations in the sea surface temperature(SST)and atmospheric circulation over the tropical central–eastern Pacific.It remarkably impacts on weather and climate worldwide,so monitoring and prediction of ENSO draw intensive research.However,there is not yet a unique standard internationally for identifying the timing,intensity,and type of ENSO events.The National Climate Center of China Meteorological Administration(NCC/CMA)has led the effort to establish a national identification standard of ENSO events,which was officially endorsed by the National Standardization Administration of China and implemented operationally in NCC/CMA in 2017.In this paper,two key aspects of this standard are introduced.First,the Ni?o3.4 SST anomaly index,which is well-recognized in the international ENSO research community and used operationally in the US,has replaced the previous Ni?o Z index and been used to identify the start,end,and peak times,and intensity of ENSO events.Second,two new indices—the eastern Pacific ENSO(EP)index and the central Pacific ENSO(CP)index,based on the SST conditions in Ni?o3 and Ni?o4 region respectively,are calculated to first determine the ENSO type before monitoring and assessing the impacts of ENSO on China’s climate.With this standard,all historical ENSO events since 1950 are consistently re-identified;their distinct properties are diagnosed and presented;and the impacts of ENSO events under different types on China’s climate are re-assessed.This standard is also employed to validate the intensity,grade,and type of the ENSO events predicted by the NCC/CMA operational ENSO prediction system.The new standard and the thus derived unified set of re-analyzed historical ENSO events and associated information provide a good reference for better monitoring and prediction of future ENSO events.

Prediction of Primary Climate Variability Modes at the Beijing Climate Center

Climate variability modes, usually known as primary climate phenomena, are well recognized as the most impor- tant predictability sources in subseasonal-interarmual climate prediction. This paper begins by reviewing the re- search and development carried out, and the recent progress made, at the Beijing Climate Center （BCC） in predicting some primary climate variability modes. These include the El Nifio-Southern Oscillation （ENSO）, Madden-Julian Oscillation （MJO）, and Arctic Oscillation （AO）, on global scales, as well as the sea surface temperature （SST） modes in the Indian Ocean and North Atlantic, western Pacific subtropical high （WPSH）, and the East Asian winter and summer monsoons （EAWM and EASM, respectively）, on regional scales. Based on its latest climate and statistical models, the BCC has established a climate phenomenon prediction system （CPPS） and completed a hindcast experi- ment for the period 1991-2014. The performance of the CPPS in predicting such climate variability modes is system- atically evaluated. The results show that skillful predictions have been made for ENSO, MJO, the Indian Ocean basin mode, the WPSH, and partly for the EASM, whereas less skillful predictions were made for the Indian Ocean Dipole （IOD） and North Atlantic SST Tripole, and no clear skill at all for the AO, subtropical IOD, and EAWM. Improve- ments in the prediction of these climate variability modes with low skill need to be achieved by improving the BCC＇s climate models, developing physically based statistical models as well as correction methods for model predictions. Some of the monitoring/prediction products of the BCC-CPPS are also introduced in this paper.

Upper-Ocean Dynamical Features and Prediction of the Super El Nino in 2015/16:A Comparison with the Cases in 1982/83 and 1997/98

The 2015/16 super El Ni？o event has been widely recognized as comparable to the 1982/83 and 1997/98 El Ni？o events.This study examines the main features of upper-ocean dynamics in this new super event,contrasts them to those in the two historical super events,and quantitatively compares the major oceanic dynamical feedbacks based on a mixed-layer heat budget analysis of the tropical Pacific.During the early stage,this new event is characterized by an eastward propagation of SST anomalies and a weak warm-pool El Ni？o;whereas during its mature phase,it is characterized by a weak westward propagation and a westward-shifted SST anomaly center,mainly due to the strong easterly wind and cold upwelling anomalies in the far eastern Pacific,as well as the westward anomalies of equatorial zonal current and subsurface ocean temperature.The heat budget analysis shows that the thermocline feedback is the most crucial process inducing the SST anomaly growth and phase transition of all the super events,and particularly for this new event,the zonal advective feedback also exerts an important impact on the formation of the strong warming and westward-shifted pattern of SST anomalies.During this event,several westerly wind burst events occur,and oceanic Kelvin waves propagate eastwards before being maintained over eastern Pacific in the mature stage.Meanwhile,there is no evidence for westward propagation of the off-equatorial oceanic Rossby waves though the discharging process of equatorial heat during the development and mature stages.The second generation El Ni？o prediction system of the Beijing Climate Center produced reasonable event real-time operational prediction during 2014–16,wherein the statistical prediction model that considers the preceding oceanic precursors plays an important role in the multi-method ensemble prediction of this super.

Enlightenments from researches and predictions of 2014–2016 super El Nio event

El Nio is a remarkable climate phenomenon with a basinwide warming of sea surface temperatures（SST） in the easterncentral tropical Pacific. El Nio means The Little Boy, or Christ Child in Spanish, and on the contrary, a basinwide cooling of the tropical Pacific SST is called La Nia that means The Little Girl in Spanish.

A Review of Research on Tropical Air–Sea Interaction, ENSO Dynamics, and ENSO Prediction in China

Remarkable progress has been made in observations,theories,and simulations of the ocean–atmosphere system,laying a solid foundation for the improvement of short-term climate prediction,among which Chinese scientists have made important contributions.This paper reviews Chinese research on tropical air–sea interaction,ENSO dynamics,and ENSO prediction in the past 70 years.Review of the tropical air–sea interaction mainly focuses on four aspects:characteristics of the tropical Pacific climate system and ENSO;main modes of tropical Indian Ocean SSTs and their interactions with the tropical Pacific;main modes of tropical Atlantic SSTs and inter-basin interactions;and influences of the mid–high-latitude air–sea system on ENSO.Review of the ENSO dynamics involves seven aspects:fundamental theories of ENSO;diagnosis and simulation of ENSO;the two types of ENSO;mechanisms of ENSO initiation;the interactions between ENSO and other phenomena;external forcings and teleconnections;and climate change and the ENSO response.The ENSO prediction part briefly summarizes the dynamical–statistical methods used in ENSO prediction,as well as the operational ENSO prediction systems and their applications.Lastly,we discuss some of the issues in these areas that are in need of further study.

Seasonal Rainfall Forecasts for the Yangtze River Basin of China in Summer 2019 from an Improved Climate Service

Rainfall forecasts for the summer monsoon season in the Yangtze River basin(YRB) allow decision-makers to plan for possible flooding, which can affect the lives and livelihoods of millions of people. A trial climate service was developed in 2016, producing a prototype seasonal forecast product for use by stakeholders in the region, based on rainfall forecasts directly from a dynamical model. Here, we describe an improved service based on a simple statistical downscaling approach. Through using dynamical forecast of an East Asian summer monsoon(EASM) index, seasonal mean rainfall for the upper and middle/lower reaches of YRB can be forecast separately by use of the statistical downscaling, with significant skills for lead times of up to at least three months. The skill in different sub-basin regions of YRB varies with the target season. The rainfall forecast skill in the middle/lower reaches of YRB is significant in May–June–July(MJJ), and the forecast skill for rainfall in the upper reaches of YRB is significant in June–July–August(JJA). The mean rainfall for the basin as a whole can be skillfully forecast in both MJJ and JJA. The forecasts issued in 2019 gave good guidance for the enhanced rainfall in the MJJ period and the near-average conditions in JJA. Initial feedback from users in the basin suggests that the improved forecasts better meet their needs and will enable more robust decision-making.

Detecting Primary Precursors of January Surface Air Temperature Anomalies in China

This study aims to detect the primary precursors and impact mechanisms for January surface temperature anomaly （JSTA） events in China against the background of global warming, by comparing the causes of two extreme JSTA events occurring in 2008 and 2011 with the common mechanisms inferred from all typical episodes during 1979- 2008. The results show that these two extreme events exhibit atmospheric circulation patterns in the mid-high latitudes of Eurasia, with a positive anomaly center over the Ural Mountains and a negative one to the south of Lake Baikal （UMLB）, which is a pattern quite similar to that for all the typical events. However, the Eurasian teleconnection patterns in the 2011 event, which are accompanied by a negative phase of the North Atlantic Oscillation, are different to those of the typical events and the 2008 event. We further find that a common anomalous signal appearing in early summer over the tropical Indian Ocean may be responsible for the following late-winter Eurasian teleeonnec- lions and the associated JSTA events in China. We show that sea surface temperature anomalies （SSTAs） in the preceding summer over the western Indian Ocean （WIO） are intimately related to the UMLB-like circulation pattern in the following January. Positive WIOSSTAs in early summer tend to induce strong UMLB-like circulation anomalies in January, which may result in anomalously or extremely cold events in China, which can also be successfully reproduced in model experiments. Our results suggest that the WIOSSTAs may be a useful precursor for predicting JSTA events in China.

Interdecadal Variations of ENSO around 1999/2000

This paper discusses the interdecadal changes of the climate in the tropical Pacific with a focus on the correspond- ing changes in the characteristics of the E1 Nifio-Southern Oscillation （ENSO）. Compared with 1979-1999, the whole tropical Pacific climate system, including both the ocean and atmosphere, shifted to a lower variability regime after 1999/2000. Meanwhile, the frequency of ENSO became less regular and was closer to a white noise process. The lead time of the equatorial Pacific＇s subsurface ocean heat content in preceding ENSO decreased remarkably, in addition to a reduction in the maximum correlation between them. The weakening of the correlation and the shorten- ing of the lead time pose more challenges for ENSO prediction, and is the likely reason behind the decrease in skill with respect to ENSO prediction after 2000. Coincident with the changes in tropical Pacific climate variability, the mean states of the atmospheric and oceanic components also experienced physically coherent changes. The warm an- omaly of SST in the western Pacific and cold anomaly in the eastern Pacific resulted in an increased zonal SST gradi- ent, linked to an enhancement in surface wind stress and strengthening of the Walker circulation, as well as an in- crease in the slope of the thermocline. These changes were consistent with an increase （a decrease） in precipitation and an enhancement （a suppression） of the deep convection in the western （eastern） equatorial Pacific. Possible con- nections between the mean state and ENSO variability and frequency changes in the tropical Pacific are also dis- cussed.

Responses of Arctic sea ice to stratospheric ozone depletion

The Arctic has experienced several extreme springtime stratospheric ozone depletion events over the past four decades,particularly in 1997,2011 and 2020.However,the impact of this stratospheric ozone depletion on the climate system remains poorly understood.Here we show that the stratospheric ozone depletion causes significant reductions in the sea ice concentration(SIC)and the sea ice thickness(SIT)over the Kara Sea,Laptev Sea and East Siberian Sea from spring to summer.This is partially caused by enhanced ice transport from Barents-Kara Sea and East Siberian Sea to the Fram Strait,which is induced by a strengthened and longer lived polar vortex associated with stratospheric ozone depletion.Additionally,cloud longwave radiation and surface albedo feedbacks enhance the melting of Arctic sea ice,particularly along the coast of the Eurasian continent.This study highlights the need for realistic representation of stratosphere-troposphere interactions in order to accurately predict Arctic sea ice loss.