Abstract:With the support of the National Key Research and Development program,the project proposed a new method for the detection and correction of inhomogeneity of the observed land surface climate data,solved the problem of the detection and correction of the gradual inhomogeneity,and constructed the station and grid data set for the homogenized surface solar radiation,air temperature,ground temperature,wind speed and precipitation in China.The conclusions on the trend of surface wind speed,warming pattern in China and its formation mechanism have been revised.Multi-source data were integrated to construct and validate historical and future datasets of key anthropogenic factors affecting natural systems such as power plants,population,biomass energy,water withdrawal,nitrogen emissions,and carbon dioxide emissions at the km,watershed,or county level.Scenarios of future key anthropogenic factors were constructed,methane and nitrous oxide emission scenarios under carbon neutrality targets and future scenarios used to drive global models were developed,and the mitigation effects of China's carbon neutrality on global warming were estimated,and it was found that China's carbon neutrality had significant mitigation effects on long-term and medium-term global warming.The safety threshold and overshoot time of water nitrogen emission in each province of China are given,the relationship between grain yield and nitrogen fertilization in China is expounded,and effective ways to reduce water nitrogen emission under the premise of ensuring food security are proposed.It is pointed out that the reconstruction of urban and rural nutrient cycling system is a necessary way to ensure food security and restore water quality at the same time.It is found that the interannual change of global water vapor deficit is significantly related to the interannual change of atmospheric carbon dioxide concentration rising rate,which illustrates the important role of water vapor deficit change in regulating ecosystem productivity and the complex influence of multi-factor coupling on ecosystem productivity change.A more comprehensive and detailed assessment of the socio-economic and natural ecological impacts of China's various pathways to carbon neutrality is recommended to ensure that the goal of carbon neutrality is achieved in synergy with other sustainable development goals.

Abstract:Under the background of global warming,extreme weather and climate events may be unprecedented when they occur with increased frequency,evident temporal and spatial clustering (concurrence),stronger persistence and new combinations (compound).This increases the difficulty of predicting extreme events.Extreme weather and climate events,such as persistent heavy rainfall,extreme low temperature,extreme high temperature and drought compound events,heat waves and strong typhoon,have produced a significant influence on economic,social and sustainable development.However,new features of extreme weather and climate events,coupled with associated key processes and relevant mechanism,remain unclear,and the prediction of extreme weather and climate events needs further improvement.This paper first gives a brief introduction of the National Key R&D Program of China for Earth System and Global Change;supported by this program,the new features of extreme weather and climate events causing serious influence over China under changing climate are investigated.Next,the paper explores the physical mechanisms of ocean-land-atmosphere coupling processes that affect extreme weather and climate events,and attempts to seek the precursors for sub-seasonal to seasonal predictions of extreme events.This program aims to develop a new statistical-dynamical combined prediction method of extreme events and establish a new generation of high-resolution numerical prediction in China,along with a detection and attribution system of extreme events.This paper also highlights recent main achievements of this program.

Abstract:Earth System Models (ESM) are powerful tools for studying the earth system and play an indispensable role in conducting scientific research on disaster prevention and mitigation,climate change,and environmental governance.Traditional weather and climate models rapidly evolve towards ESM,including ocean,sea ice,biogeochemical,and atmospheric chemical processes.At the same time,an increasing number of applications are adopting ESM for weather,climate,and ecological prediction.The current international mainstream trend in developing numerical models is to achieve seamless simulation and prediction by constructing integrated models,simultaneously meeting the needs of weather-climate forecasts and predictions at varying temporal and spatial scales.With improved model complexity and resolution,traditional numerical weather models have rapidly progressed in climate change research and climate prediction.However,challenges remain regarding data assimilation,ensemble coupling,high-performance computing,and uncertainty analysis and evaluation.The combination of artificial intelligence (AI) and meteorology has recently attracted tremendous attention.Based on various deep learning architectures,deep learning models can be trained using powerful computing resources and massive data for weather forecasts in a new scientific paradigm independent of traditional numerical weather models.Some technology companies,such as Huawei,NVIDIA,DeepMind,Google,Microsoft,etc.,as well as domestic and international universities such as Tsinghua University,Fudan University,the University of Michigan,Rice University,etc.,have released several Large Weather Models (LWMs) covering from nowcasting,short-term forecast to medium-term forecast,and even extended-period forecast.For instance,FourCastNet,GraphCast,NowcastNet,Pangu Weather,Fengwu,Fuxi,etc.,show significant advantages and great potential in improving forecast accuracy and accelerating the forecast inference process.For accuracy,except in areas like extreme weather,LWMs have matched or even surpassed that of traditional numerical models.Moreover,with continuous development of deep learning methods,their forecasting precision is steadily increasing.For timeliness,LWMs,leveraging deep neural networks' powerful generalization capabilities,far exceed traditional numerical models' predictive abilities under the same resolution conditions.For computational speed,LWMs have significantly increased inference computation speed compared to traditional numerical models,gradually reduced the enormous computation times required by traditional numerical models.The emergence of LWMs signifies that the cross-fertilization between AI and meteorological fields has reached a new horizon.Although these LWMs have made significant breakthroughs at this stage,their development still faces many challenges,such as the interpretability problem,the generalization and migration challenge,and the over-smoothing problem.The advancement of numerical weather prediction is closely tied to developments in computational and data storage technology,as well as observational techniques.Its application requires interdisciplinary integration,combining insights from various scientific fields.A critical scientific challenge in this field is to foster a more profound integration of numerical weather prediction with emerging information technologies such as artificial intelligence,quantum computing,and digital twins.This challenge also involves tailoring complex and refined component models to meet diverse disciplinary demands and societal needs.Advancing numerical weather prediction within the broader context of earth system science requires a concerted effort to promote cross-disciplinary collaboration,addressing vital scientific questions at the intersection of multiple fields.

Abstract:At present,precipitation forecasting mainly relies on numerical weather forecasting models.However,due to factors such as physical parameterization and computational resources,there remains significant uncertainty in precipitation forecasting based on numerical models.In recent years,deep learning has shown great advantages and potential in the field of weather forecasting.The present study constructs neural networks to predict daily precipitation distribution in the northeastern United States,to explore the capabilities of neural-network models in predicting high-resolution precipitation (CPC,0.25°) using low-resolution meteorological fields (ERA-Interim,0.7°).Next,the study compares the performance of three mainstream network frameworks (VGG,ResNet,and GoogleNet) in the aforementioned task.The results indicate that all three frameworks have certain capabilities for predicting the daily precipitation distribution in the northeastern United States,with VGG performing the best,but their root mean square error (RMSE) is higher than that of the ERA-Interim 24-hour (ERA24) prediction.The ensemble-mean results of the three neural networks are all superior to the ERA24 prediction,and combining these three with the ERA24 prediction results can significantly improve ERA24 prediction in different seasons and intensities.It is thus concluded that deep learning has great potential in improving the resolution and accuracy of precipitation prediction.

Abstract:Prediction of tropical cyclone (TC) genesis at the extended-range to subseasonal timescale (a week to several weeks) is a gap between weather and climate predictions,which is a challenge for TC forecast.This study presents an extended-range hybrid dynamical-statistical prediction model and a statistical prediction model for TC frequency over the western North Pacific.The models are based on tropical intraseasonal oscillation signals and the TC clustering method.The fuzzy c-mean clustering method categorizes TCs over the western North Pacific into seven track patterns.Predicting anomalous TC counts in each week involves adding the observed climatological mean of weekly TC counts to obtain total genesis counts for each cluster.The probability of TC track distributions each week is derived by involving the climatology of each track probability.This model could not only predict TC number for each cluster but also the TC track distribution pattern each week.The hybrid dynamical-statistical model relies on contemporaneous statistical relationships between low-frequency variabilities and the output of the ECMWF dynamical model from the S2S dataset.The predictand is the TC genesis number over the western North Pacific during each week.Evaluation of prediction results indicates that the forecast skill of the hybrid dynamic-statistical forecast surpasses that of the statistical forecast model.The precursor signals associated with sub-seasonal TC changes dissipate rapidly,making stable forecasts challenging.In contrast,the dynamic model simulates the low-frequency background field (predictors) effectively,enhancing the hybrid model's forecast skill.While,the current forecast skill of the hybrid dynamic-statistical forecast model extends to six weeks,further improvement is possible.Evaluation of prediction skills and error analysis of different TC clusters reveal that interannual and interdecadal variabilities of background fields on the modulations of intraseasonal oscillations on TC activity cannot be ignored.Statistical relationships between TC counts and low-frequency variabilities differ in distinct ENSO phases,suggesting potential improvement by developing forecast models based on different ENSO phases.Additionally,extratropical intraseasonal signals (e.g.,Rossby wave breaking and westerly jet intensity) significantly impact TC frequency and trajectory,which may provide more source of predictability for TC extended-range prediction.

Abstract:In this study,a Rapid Refresh Hybrid system was constructed based on the Weather Research and Forecasting (WRF) model,WRF Hybrid Data Assimilation system,and Ensemble Transform Kalman Filter (ETKF),while assimilating both Wind Profile Radar Detection (WPRD) and Microwave Radiometer (MWR) data.Experiments were performed on the impact of four important parameters on the system (that is,two tuning factors of static background error,localization scale and ensemble weighting factor),and contrast research was carried on to the results of the hybrid and 3DVAR schemes.Some encouraging conclusions were reached:Tuning these four parameters could improve performance of the Rapid Refresh Hybrid system,the analysis and forecast of the hybrid scheme with parameters not tuned were superior to those of 3DVAR,and the best results were those of the hybrid schemed with parameters tuned.

Abstract:In this study,NCEP/FNL reanalysis and WRFV3.9.1 are used to investigate the influence of the intensity of the monsoon depression on the rainstorm process in Guangdong from 27 August to 1 September,2018.On August 27,the rainstorm was in the development stage,the precipitation distribution was relatively scattered,and the maximum value of the daily precipitation was 80 mm/day.Starting from August 28,the heavy rainfall was concentrated and tended to shift southward to the Pearl River Delta region,with a maximum daily precipitation exceeding 100 mm/day.The rainstorm reached a mature phase between 29 and 30 August,with a maximum of daily precipitation more than 200 mm/day,and the location of the precipitation area was concentrated in the southeast coastal area of Guangdong,with a northeast-southwest spatial distribution.On 31 August,the rainfall began to weaken,but the daily precipitation remained strong.Above all,this rainstorm process involved much precipitation,a long duration,and a concentrated area,making it a rare extraordinary heavy rainstorm event.Using the physical decomposition method indicates that the monsoon depression and low-level jet provided sufficient water vapor and energy for the rainstorm process.In the mature phase of the rainstorm process,there was a negative entropy anomaly to the south of the rainstorm area,and a positive one to the north.The zero line of entropy anomaly indicates that the tendency of entropy was zero,that is,the value of entropy reached its peak,which means that the accumulation of energy had a maximum value in this area.On the north side of the rainstorm,the increase of the entropy means that the unstable energy continued to grow,while the precipitation on the south side released the unstable energy,that is,the entropy decreased and tended to be stable.The location of the zero line of the entropy change corresponded closely with the spatial distribution of the precipitation.Energy accumulating at the zero line of the entropy change was favorable to the development of the rainstorm.To further verify the influence of the monsoon depression,sensitivity experiments (EXP_500,EXP_700,and EXP_1000) were constructed.Sensitivity experiments were conducted by filtering out the perturbation component in the monsoon depression to change the intensity of the monsoon depression.The results show that when the monsoon depression was strong,the rainstorm was likely to occur and the precipitation was strong,and when the monsoon depression was weak,the precipitation was reduced or even absent.The diagnostic analysis shows that the energy helicity had implications for the development of the rainfall:when the energy helicity was greater,the intensity of precipitation was stronger,and when the intensity of the monsoon depression weakened,the energy helicity decreased and the precipitation weakened.

Abstract:During the period of 1979—2019,there was a significant negative correlation between the interannual variation in precipitation anomalies over South China (SC) and those in the eastern Tibetan Plateau (ETP) in July,with a correlation coefficient of -0.60 between the area-averaged precipitation over the two regions.It is statistically significant at the 99％ confidence level.Therefore,a precipitation dipole index (PDI) is defined as the difference in the standardized area-averaged precipitation between SC (20°—26°N,108°—123°E) and the ETP (28°—35°N,90°—108°E) in July.The positive phase of the July precipitation dipole is characterized by increased (decreased) precipitation over SC (the ETP),and vice versa.Based on this,we utilize ERA5 reanalysis data and station precipitation data from the period of 1979—2019 to investigate the influence of soil moisture anomalies in May on the interannual variation of the July precipitation dipole,along with its underlying physical processes.The study results show that higher soil moisture in the TP and lower soil moisture in Central China (CC) in May tend to lead to increased (decreased) precipitation over SC (the ETP) in July,displaying a dipole pattern of precipitation over the two regions.Further analysis reveals that the positive (negative) soil moisture anomalies in the TP (CC) may persist from May to July,resulting in positive surface turbulent heat flux anomalies in the Northern China (NC) region in July.This in turn causes local middle to lower troposphere warming,thus intensifying the meridional temperature gradient and atmospheric baroclinicity between NC and Lake Baikal.Correspondingly,enhanced synoptic-scale transient wave activity is observed,which creates an anomalous high pressure with an equivalent barotropic structure and Rossby wave sources over NC-Mongolia by transient vorticity forcing.The associated Rossby waves propagate southeastward to SC.Subsequently,an anomalous low pressure appears over SC with an equivalent barotropic structure,which facilitates the eastward shift of the western Pacific subtropical high and westward shift of the South Asian high.This anomalous circulation respectively corresponds to anticyclone and cyclone anomalies in the middle and lower troposphere over Mongolia and SC,and consequently enhanced (suppressed) precipitation occurs in SC (the ETP).In addition,the positive feedback process between the anomalous high over Mongolia and local low soil moisture is favorable to the formation of the July precipitation dipole by intensifying and maintaining the above physical process,and vice versa.

Abstract:A heat wave defined as several consecutive days with positive temperature anomalies.Since the 1950s,heat waves have increased in number and intensified globally,exerting significant impacts on human health,theecosystem,and the social economy.The increase in heat waves,especially extreme events with longer duration and higher amplitude over North China after the late 1990s,is of particular note.However,relevant existing studies have mainly focused on unsorted events or typical cases,and the atmospheric backgrounds of heat waves with different severities are unclear.Therefore,using the CN05.1 daily maximum temperature and NCEP/NCAR reanalysis data for the period of 1961—2020,this study identifies severe and mild heat wave events over North China,then selects the years with high frequencies of such events.On this basis,the spatiotemporal variation and atmospheric background of severe and mild heat waves events over North China are investigated through composite analysis.The results show that high frequencies of severe and mild heat wave events mainly appear in the northern part and the middle and southern parts of North China,respectively.For both types of events,their frequencies averaged over North China have shown a significant increasing trend since the 1960s,with a higher occurrence rate of severe heat waves (0.09 events per decade) than mild ones (0.03 events per decade).Corresponding to high frequencies of severe heat waves in North China,an anomalous anticyclonic circulation with the quasi-equivalent barotropic structure dominates the mid-latitudes of East Asia,and the North China domain is located in its southeast flank.Against this background,anomalous subsidence and hence reduction of cloud cover and humidity appear in North China.In addition to the adiabatic heating of the subsidence,the clear-dry air over North China is conducive to stronger solar radiation reaching the land surface.Accordingly,the outgoing longwave radiation and sensible heat from the land surface are enhanced,thus warming the overlying atmosphere over the region.Incomparison,associated with high frequencies of mild heat wave events in North China is an anomalous dipole pattern with the anticyclonic and cyclonic anomalies respectively residing on the southeast and northwest sides of North China,and the target region is under the control of southwesterly anomalies.The southwesterly anomaly can heat the atmosphere through warm advection transport toward North China.At the same time,it also increases humidity over North China by transporting water vapor from the ocean to the region.The increased humidity may capture and reflect more longwave radiation onto the surface,thus favoring the warming of overlying atmosphere.Ingeneral,the occurrence of severe heat waves is affected by the joint contribution of adiabatic heating due to abnormal subsidence,solar radiation and sensible heating,exhibiting a dry-hot nature.The occurrence of mild heat waves is dominated by the greenhouse effect induced by abundant water vapor,and horizontal temperature advection,exhibiting a wet-hot nature.There is a discernible difference in the atmospheric background of severe and mild heat waves.

Abstract:Atmospheric rivers (ARs) are characterized as long,narrow,and transient channels of strong horizontal water vapor transport.Previous studies have primarily focused on their impact on mid-latitudes,emphasizing the potential risks of deleterious hazards and financial losses.However,less attention has been given to ARs in the Antarctic region,despite they account for over 90％ of moisture transport into the high latitudes.ARs typically originate in the robust poleward meridional transport flank within ridges (blocking highs) and explosive extratropical cyclones in the Antarctic region,facilitating the substantial moisture transport through a vigorous low-level jet.Three widely-used metrics for characterizing the moisture intrusion state are integrated water vapor,v-component of integrated water vapor and integrated water vapor.An AR is detected when the enclosed shape of the extremely high moisture intrusion path is adequately elongated.The frequency of ARs varies across different AR detection algorithms based on diverse metrics and distinct extremity thresholds.The annual frequency of ARs decreases with latitude,exhibiting a zonally asymmetric pattern that show higher seasonal frequencies in austral winter and spring.These spatial and temporal features are shaped by the geographical environment and the distribution of synoptic systems in the Antarctic region.The annual variability of AR frequency appears to be associated with dominant atmospheric modes in southern high latitudes,such as the Southern Annular Mode.Additionally,it is also modulated by the natural variability of sea surface temperature modes,including El Niño Southern Oscillation and Indian Ocean Dipole.ARs have significant impacts on the Antarctic ice sheet and sea ice.ARs contribute both positively and negatively to the Antarctic ice sheet.On one hand,the intense snowfall during ARs constitutes a major portion of the total precipitation over the ice sheet,favoring its mass gain.Conversely,warm-moist air intrusions accompanying ARs induce surface melting and extremely high temperatures due to foehn winds and anomalously high net surface energy flux.Moreover,surface meltwater during ARs promotes hydraulic fracturing on the ice shelves and trigger their disintegration by removing sea ice through strong winds.These processes pose a substantial threat to the ice sheet mass balance.Meanwhile,the warm-moist air and strong winds during ARs thermodynamically and dynamically reduce sea ice.ARs lead to anomalously high temperatures and net surface heat flux,intensifying sea ice thermodynamically melting,especially in winter.The strong winds dynamically drift the sea ice onshore,accelerating ice breaking through powerful waves and further enhancing lateral melting.Though ARs in the Antarctic region have been subject to various analyses,certain questions persist.The evaluation on ARs' impact on Antarctic ice sheet and sea ice remain contingent on the chosen detection algorithm,necessitating the development of a more universal and robust approach.Implementing machine learning to extract the spatial and temporal features of ARs could offer such an approach.Moreover,although the fact that most liquid precipitation is attributed to ARs,its influence on the ice sheet and sea ice is often overlooked,despite its potential to enhance melting and destabilize ice shelves.In addition,ARs may exert a profound influence on the ocean,subsequently providing feedback to the atmosphere.However,the interaction between ARs and the Southern Ocean is not well understood.Therefore,further research imperative to elucidate these mechanisms and evaluate the future changes in Antarctic ice sheet mass balance and sea ice influenced by ARs.

Abstract:Atmospheric detection lidar has been widely used in air pollutant monitoring,aerosol detection,cloud parameter retrieval,boundary layer height inversion and other important fields due to its high detection accuracy,fine time resolution and far vertical detection ability.In recent years,many new ground-based lidars have been applied to observation networks in China,providing many continuous vertical-profiling observations.However,the quality of ground-based lidar data is inconsistent,thus the quality control of lidar detection data is important.Specifically,unrealistic near-surface aerosol profiles can be found in some lidar products,indicating that the calibration of overlap function is crucial to improving the data quality of ground lidars.The experimental method proposed by Wandinger and Ansmann (2002),based on observations from two channels,is widely used to determine the overlap function of Raman lidars when external calibration instruments are unavailable.This method assumes that the overlap function in the elastic channel is approximately equal to that in the nitrogen channel.This assumption is valid when the lidar system is well aligned,thus Wandinger and Ansmann (2002) did not create a method to determine it.However,misalignments often occur in practice.After a lidar is initially calibrated after production,slight vibrations in the process of transportation,handling and installation may lead to misalignments,which may result in the precondition for the overlap function calibration method being invalid.It is unclear whether the method proposed by Wandinger and Ansmann (2002) can be used to calibrate the overlap function in reality,thus this paper introduces a telecover test to evaluate the validity of precondition for the calibration method.A ray tracing method is applied to the Raman lidar at the Nanjing Meteorological Bureau,and simulations are conducted to determine the overlap functions of both the elastic and nitrogen channels under various alignment conditions and during different telecover tests.The simulation results reveal that the precondition of the method proposed by Wandinger and Ansmann (2002) is valid when the ratio of elastic signals to nitrogen signals remains consistent across different quadrants of the telescope aperture.However,the precondition can be invalid when the ratio of elastic signals to nitrogen signals differs during different telecover tests.Using this method,the Raman lidar of Nanjing Meteorological Bureau was evaluated and adjusted.After the Raman lidar was initially installed,the results from telecover tests show that the signal strength of elastic scattering channel differs greatly from that of nitrogen channel.A test using the CCD-lidar (charge-coupled device lidar) system was applied to verify the overlap function determined by the dual-channel experimental method,and the results suggest that the error is high due to the invalidity of the precondition.To solve this problem,we adjusted the lidar optical system to improve its collimation.After this,we performed the telecover test again,and the signal strength of elastic scattering channel was basically the same as that of nitrogen channel.Our numerical simulations suggest that the preconditions of Raman radar overlapping function calibration method can be basically satisfied after the adjustment.The CCD-lidar test was performed again,and this time the overlap function determined by the dual-channel experimental calibration method was constant with CCD-lidar.The method proposed in this paper can effectively verify the applicability of the dual-channel experimental calibration method,and has good operability,thus this method could be applied to other Raman lidars in the future.Compared with the CCD-lidar observation calibration method which requires outdoor operations at clear-sky nighttime,the dual-channel experimental calibration method has greater advantages when the preconditions are met.