Abstract:The terrestrial ecosystem can absorb about 30％ of the anthropogenic carbon emissions through vegetation photosynthesis,which plays an important role in the global carbon cycle and slowing the rise of atmospheric carbon dioxide concentration.The solar-induced chlorophyll fluorescence (SIF) remote sensing technology developed in the last 10 years can monitor the actual photosynthesis of vegetation,providing new ideas and methods for the study of global terrestrial ecosystem carbon cycle.This paper reviews the recent progresses in the study of satellite-based SIF observations and their applications in terrestrial ecosystem carbon cycling and land-atmosphere interaction,especially in the estimation of global vegetation gross primary productivity and the development of terrestrial ecosystem carbon cycle models,and further discusses the challenges faced by research in this field and the future development directions.

Abstract:Nowadays,ensemble forecasting has become the main support for operational weather forecasting.However,due to the limitations and imperfections of the numerical model itself,as well as the limitations of the ensemble forecast system in terms of initial perturbation schemes,ensemble size,etc.,the forecast results are generally biased.In addition,different forecasting models usually have different physical parameterization schemes,initial conditions,etc.,resulting in different forecasting capabilities.Therefore,how to correct the forecast deviation and how to make full and effective use of forecast information from different models to obtain more accurate weather forecasts has received extensive attention.In recent years,using statistical theory and forecasting diagnosis,multimodel ensemble forecasting technologies based on multiple ensemble prediction systems have been rapidly developed,and has become a statistical post-processing method to effectively eliminate forecast deviation and improve weather forecasting skills.For the three most basic surface meteorological variables (i.e.,temperature,precipitation and wind),the widely used multimodel ensemble technologies such as ensemble mean (EM),bias-removed ensemble mean (BREM),superensemble (SUP),Bayesian model averaging (BMA),and ensemble model output statistics (EMOS) are first introduced from the perspective of deterministic forecasting and probabilistic forecasting.Finally,this paper discusses the issues that need to be paid attention to when using and developing multimodel ensemble technologies,including considering the number of participating models,developing precipitation and wind speed classification forecast models,and developing new multimodel ensemble technologies based on machine learning.

Abstract:Based on the content of Chapter 7 from the Sixth Assessment Report (AR6) contributed by the Intergovernmental Panel on Climate Change (IPCC) Working Group I (WGI),this paper interprets the dependence of climate feedbacks on temperature patterns in detail.Compared with the Fifth Assessment Report (AR5),the understanding of the relationship between transient changes in climate feedback and evolving spatial patterns of surface temperature has been greatly improved in AR6.The assessments show that under greenhouse gas forcing,it is very likely that the warming in the Arctic will be more pronounced than on global average over the 21st century.On centennial timescales,the Antarctic will warm more than the tropics and the tropical Pacific Ocean east-west sea surface temperature (SST) gradient will weaken,with greater warming in the east than the west.AR6 identifies changes in the degree of polar amplification over time,particularly in the Southern Hemisphere,and changes in the tropical Pacific Ocean east-west SST gradient over time as key factors affecting how climate feedbacks may evolve in the future.Climate feedbacks,particularly from clouds,are expected to become less negative (more amplifying) on multi-decadal timescales as the spatial pattern of surface warming evolves.

Abstract:The Yangtze River Basin(YRB) is one of the areas with a high frequency of heatwave occurrences in China.The daily maximum temperature (T_max) in this area shows significant low-frequency oscillation signals for (10—30 d and 30—60 d) time periods.Based on the results of the lead-lag correlation analysis between the YRB T_max and the 10—30 d/30—60 d convection and circulation anomalies,we identify the main low-frequency signals affecting the YRB T_max.There are three types of signals that travel in different directions:1) the eastward and southward signals from the Eurasian continent;2) circulation anomalies propagating southwestward from Northeast Asia;and 3) low-frequency convective signals propagating from the western Pacific toward East Asia.The temperature diagnostic equation results show that when the low-frequency convection/circulation anomalies approach the YRB,both the diabatic (clear-sky radiative heating) and adiabatic (associated with sinking motion) heating processes lead to variations in the YRB temperature.To identify these precursory signals objectively and efficiently,as well as consider the nonlinear interaction between YRB T_max and the large-scale predictors,we use Convolutional Neural Network (CNN),a type of deep neural network,to train the historical data,and then develop an extended-range forecast model for YRB T_max.The independent forecast results show that the CNN-based forecast model is capable of predicting the YRB T_max at a 30-day lead time,with the temporal correlation coefficient between the forecast and observed T_max of 0.63—0.70 (exceeding the 99％ confidence level).The current results suggest the potential of CNN in the application of extended-range forecasting as the magnitude of error (root-mean-square error) is less than one standard deviation.

Abstract:Precise weather monitoring and accurate weather forecast are two of the most decisive factors for the success of the Winter Olympics.Considering the particularity of the 2022 Beijing Winter Olympic Games (the only Winter Olympic Games held under the climate dominated by the continental East Asian winter monsoon and the only Winter Olympic Games held in inland areas) and the rigid demand for the goal of “hundred-meter resolution and minute-updated level” high-precision forecast,the Beijing Institute of Urban Meteorology has developed a new generation of the Rapid-refresh Integrated Seamless Ensemble system—RISE—that can provide 500-and even 100-m resolution spatial grid forecast data products with 10-min updated frequency for the Beijing Winter Olympics.In order to improve the prediction accuracy of the RISE system,and considering the successful use of deep learning in the field of geoscience in recent years,this paper develops a convolution neural network-based model,Rise-Unet,using the high-resolution RISE data from 2019 to 2021 to correct the prediction results of 2-m surface temperature,2 m-relative humidity,10-m U wind speed,and 10-m V wind speed for a lead time of 4—12 hours.The root-mean-square error and mean absolute error are employed to evaluate the accuracy of the model in this study.By comparing with the original prediction results of the RISE system,it is proven that the deep learning-based model,Rise-Unet,can effectively improve the accuracy of high-resolution gridded prediction results.The method proposed in this study can be applied as the post-processing module of the RISE system,which has important scientific significance and application value for improving the grided prediction level of the RISE system as well as other high-resolution numerical weather forecasting systems.

Abstract:Based on the observations of daily snowfall,precipitation,temperature,relative humidity,air pressure and wind speed in China,a distinguishing method for the snowfall events based on the Logistic regression approach is constructed,and the applicability of this method and other widely used snowfall distinguishing methods is compared.Results show that the single temperature threshold and S-curve methods are relatively uncertain for snowfall simulation within the temperature range from -3 ℃ to 4 ℃.By contrast,the series of Logistic fitting methods have higher success rates in determining snowfall events,and are more robust for snowfall recognition in different regions of China,especially in the Tibetan Plateau.In the Logistic methods,temperature and relative humidity play a decisive role in determining snowfall,while the influences of air pressure and wind speed are relatively small.The Logistic wet-bulb temperature scheme (LogT_w) and the air temperature+relative humidity scheme (LogT_aH_R) can well reproduce the spatial distribution and interannual variation characteristics of snowfall,and the corresponding deviationsare smaller than other methods.On the whole,there is little difference between the two schemes for snowfall recognition.Therefore,the LogT_w or LogT_aH_R scheme can be used to identify snowfall events in China,especially the snowfall events in climate models.

Abstract:As one of the storage methods of water resources on the earth,snow cover has an important impact on soil moisture and freshwater distribution.The north-south crossing latitude in China is about 50°,and snow cover is widely distributed.It is of great significance to study the spatial and temporal dynamics of snow cover.Based on the snow cover data of FY-3 meteorological satellite,this paper proposesa trend extraction method based on empirical mode decomposition,and discusses the spatiotemporal dynamic change trend of snow cover in China from 2010 to 2019.Results show that:1) Snow cover frequency (SCF) in China has significant seasonal characteristics,which increases first and then decreases.The SCF reaches the maximum in February and March every year.The SCF in Northeast China shows an interannual trend of significant decline,while that in other regions does not change much.2)The overall snow cover rate (SCR) in China (NeiMonggol and Tibet) decreases by about 1.2％ (about 1.5％),while that in other regions does not change significantly.The SCR in main snow cover areas changes from increasing to decreasing in 2016.

Abstract:Using the output data of 47 CMIP6 models and the NCEP/NCAR reanalysis data from 1958 to 2014,this paper studies seasonal variation characteristics of interhemispheric oscillation (IHO) in the model atmosphere,and evaluates the ability of CMIP6 to simulate the seasonal characteristics of IHO.Results show that all 47 CMIP6 models can simulate the seasonal evolution characteristics of IHO,but there are some differences among the models.Through comparison,16 better models for simulating the seasonal cycle of IHO are selected,which can successfully simulate the temporal evolution and spatial structure of hemispheric atmospheric mass.Further analysis shows that water vapor can counteract the seasonal variation of IHO,and the change of water vapor mass in the hemispheres can drive the generation of cross-equatorial mass flow.The surface net short wave radiation is higher in summer and lower in winter,and the water vapor evaporation caused by its heating plays an important role in the change of water vapor mass.The surface net long wave radiation changes greatly in spring and autumn,which is consistent with the monthly change of atmospheric mass.Compared with the reanalysis data,it shows that the peak valley variations of hemispheric atmospheric mass simulated by CMIP6 models have obvious monthly deviations,and the deviations of surface pressure anomalies simulated by CMIP6 models mainly occur in the North Pacific,Eurasia,mid-latitude regions of the Southern Hemisphere and polar areas.There are obvious deviations in the simulated evaporation and precipitation,equatorial wind field,surface net long wave and short wave radiation fluxes in the Northern and Southern Hemispheres.

Abstract:In this paper,the environmental meteorological stations in Xi'an are classified into urban,suburban,and two types of rural stations based on the proportion of construction land and the information entropy of land use.Following that,the urban-rural distribution characteristics of PM_2.5 are then explored,as well as the correlation between the urban-rural PM_2.5 concentration difference and the urban heat island effect intensity (UHII).Results show that the urban-rural distribution characteristics and the diurnal variation characteristics of PM_2.5 in Xi'an vary with the season.The PM_2.5 concentrations of the two types of rural stations are significantly different.Evidently,the correlation coefficients between UHII_D and PM_2.5 concentration of urban (rural D) stations are higher than those between UHII_U and PM_2.5 concentration of urban (rural U) stations.Furthermore,because of more emissions,the correlation coefficients between UHII_U (UHII_D) and PM_2.5 concentration of rural stations are greater than those between UHII_U (UHII_D) and PM_2.5 concentration of urban stations.UHII_D-RUPII_D demonstrates a negative correlation in the spring,summer and autumn as UHII_D increases,with the relative urban-rural PM_2.5 concentration difference (RUPII_D) showing a downward trend.In addition,the urban area's vertical diffusion capacity of PM_2.5 is greater than its horizontal diffusion capacity the larger the UHII_D.By changing the transmission and diffusion characteristics of PM_2.5,UHII_D further influences RUPII_D in Xi'an.

Abstract:In this paper,a southwest vortex rainfall process of 8—9 July 2010 over Sichuan Basin is simulated using the Mesoscale Weather Research and Forecasting (WRF) model.The simulation results are then utilized to estimate the entrainment rate in convective clouds under 0 ℃ layer.It is found that the vertical distribution characteristics of simulated precipitation particles are roughly consistent with the observed characteristics,proving that the WRF accurately simulates the vertical distribution of precipitation particles in the southwest vortex precipitation cloud system.The simulation results show that the liquid water content,vertical velocity and buoyancy in the clouds increase with height above the cloud base and decrease with height close to the cloud top.However,the moisture static energy (MSE) mainly reduces with cloud height.In the case of entrained air far away from the cloud edges,the entrainment rate decreases with height above the cloud base and increases with height towards the cloud top,while the intensity of the entrainment rate towards the cloud top weakens,and the mean entrainment rate decreases.In addition,the entrainment rate is negatively correlated with both cloud water content and rain water content in cloud,indicating that the entrainment increases the evaporation of cloud droplets,reduces the size of droplets and consequently suppress the development of convective clouds and precipitation.When we assume that the entrained environmental air surrounds the cloud edge,the estimated entrainment rate is higher than the corresponding value when we assume that the entrained air is far away from the cloud edge.However,the vertical distribution characteristics of entrainment rate and the influence of entrained air on the clouds are similar under the two assumptions.Both assumptions have advantages and disadvantages that should be considered when determining the entrapment rate of the southwest vortex cloud system.Moreover,the entrainment rate decreases over time,which is related to the development and growth of the cloud ensemble during this time.The results in this paper provide a theoretical basis for further research on entrainment rate parameterization in the Sichuan basin.

Abstract:At present,a Regional Ensemble Prediction System has been developed by the Kelamayi Meteorological Bureau.The system has only adopted the initial condition perturbation of the Ensemble Transform Kalman Filter (ETKF),which causes the system lack spread.In order to improve the performance of this Ensemble Prediction System,the model perturbation method of Stochastic Physics Parameterization Tendency (SPPT) is adopted and tested.Firstly,SPPT scientific parameters for the system are determined and set using a sensitivity test on the critical parameters of SPPT.Secondly,an ensemble forecast experiment test based on the Kelamayi Ensemble Prediction System is conducted to compare the ETKF initial perturbation scheme with the combination of the ETKF initial perturbation and SPPT model perturbation (ETKF-SPPT) scheme.The results show that the ETKF method provides initial condition perturbations with dynamic structure,while the spread saturates within a short forecast lead time and decreases due to the constraint of identical Lateral Boundary Condition (LBC) for all members.Adopting the SPPT model perturbation method significantly improves the ensemble spread for all forecast lead times.Based on the ensemble verification,adding model perturbation to initial perturbation condition improves the probabilistic forecast skill with a higher forecast reliability and a smaller RMSE.Without model perturbation,ETKF’s reliability is low and its root mean square error (RMSE) is relatively large.Additionally investigated and examined is a local gale case that occurred in Kelamayi during the experimental period.Its results show that employing model perturbation significantly improves the ensemble forecast,resulting in a more accurate forecast of the local gale’s magnitude and duration.

Abstract:The sensitivity of the structure and strength of a squall line in the initial and early development stages to initial low-level humidity and environmental vertical wind shear is investigated in a two-dimensional idealized squall line simulation using the WRF model.The results of the sensitivity test of low-level humidity indicate that increasing low-level humidity is favorable for convective triggering and stronger convective systems.The increased squall line intensity and upward movement make it easier for new convective cells to form at the leading edge of the cold pool.The increased low-level humidity also leads to more precipitation,which raises the intensity of the cold pool.In addition,the results of the sensitivity test of low-level environmental vertical wind shear show that the convection is easier to become downshear tilted in the initial stage with stronger low-level environmental vertical wind shear,which is unfavorable for convective triggering.According to RKW theory,the interaction between cold pool and low-level environmental vertical wind shear is considered as an important mechanism for the development of squall lines.The cold pool is relatively weak during the early stages of squall line development,and with weaker low-level environmental vertical wind shear,it is easier for the convection to become upright,resulting in a stronger and deeper upward movement that is favorable to the strength of the squall line.

Abstract:In this study,numerical simulation and temporal-spatial analysis are carried out on the typical typhoon-induced ocean eddy and offshore anti-cyclonic eddy in the Yellow Sea in order to investigate its three-dimensional structural characteristics,energy transference,energy conversion,and influence mechanisms of the ocean eddies.Utilizing the advantage of simulating small and meso-scale offshore ocean systems,the FVCOM (Finite Volume Community Ocean Model) regional ocean numerical model is employed as the model.According to the simulation results,the core component of both the cyclonic and anti-cyclonic ocean eddies transfers less energy than the asymmetric strong current,which also has a longer sustaining time and a deeper transmission depth.Due to the convergence of Ekman flow,this difference is more obvious in the anti-cyclonic ocean eddy.The kinetic energy of typhoon wind stress and the available potential energy of the ocean eddy are mostly converted to create the kinetic energy of the cyclone eddy.Since the wind speed over the anti-cyclonic eddy region is low,the anti-cyclonic eddy system maintains a low level of kinetic energy,and its intensity increases according to its available potential energy.The influence mechanism of environmental factors is mainly in three aspects:wind and waves,bottom friction and terrain.The results show that adding the coupled wave module allows the combined effects of typhoon strong wind stress and wind waves to increase the magnitude of the typhoon-induced ocean eddy,speed up the eddy circulation,and decrease the adjacent anti-cyclonic eddy.The wind wave contributes positively to the typhoon-induced ocean eddy.During strong typhoon processes,the surface circulation of the typhoon ocean eddy reacts to typhoon stress.and the domain topography and bottom friction both have a significant impact on the lower portion of the eddy structure.The top and bottom features cause the structure of the ocean eddy to shift vertically,slowing down bottom circulation and producing an opposing flow direction between the upper and lower part of the eddy region.Under the mixed layer,there is high temperature and salt content,and the upwelling responds to the strong divergence areas of the cyclonic and the anti-cyclonic ocean eddies,while the downwelling react to the convergence areas of the eddy regions.Meanwhile the rolling topography changes the temperature and salt levels of the upwelling and downwelling,with their intensity corresponding to the terrain scale.