Abstract:Extreme heat events and heat waves pose an increasingly significant threat to human communities,affecting public health,agriculture,economic stability and fueling secondary disasters such as wildfires.In recent years,heat waves have become more frequent,intense,and prolonged,particularly in the densely populated Northern Hemisphere (NH).However,the primary spatial modes of heat waves across the NH,along with their associated oceanic and atmospheric conditions remain insufficiently understood.This study investigates the natural variability of NH heat waves during boreal summer over the past century.We identify three leading modes in the frequency of daily maximum temperatures exceeding 35 ℃,collectively accounting for 52.6％ of the explained variance.The first mode presents a uniform pattern of heat wave frequency anomalies across most of the NH.This interdecadal mode corelates with the Atlantic Multi-decadal Oscillation (AMO),which triggers atmospheric anticyclone anomalies in the upper troposphere,decreasing cloud cover and increasing surface diabatic heating.AMO-induced Rossby wave energy propagates from low to mid-latitudes,then eastward across Eurasia and North America,establishing wave-train anomalies linked to this primary mode via teleconnections.The second mode,showing a latitudinal tripole pattern across Eurasia and a meridional dipole over North America,reflects interannual atmospheric variability tied to the North Atlantic Oscillation (NAO).The NAO influences critical North American regions through high-pressure ridges and propagating wave trains.The third mode captures an Eurasian meridional tripole and North American latitudinal dipole pattern,shaped by the Pacific Decadal Oscillation (PDO),El Niño-Southern Oscillation (ENSO),and sea surface temperature anomalies of the South Indian Ocean (SIO).Both PDO and ENSO affect NH heatwave frequency anomalies through upper-to-lower level geopotential height variations over Eurasia on interdecadal and interannual timescales,respectively.ENSO's influence extends to NH heat wave patterns via the Pacific-North America (PNA) teleconnection and the Indian Ocean capacitor effect.The SIO modulates vertical atmospheric motion over regions such as East Asia and eastern North America via Walker and Hadley circulations,further affecting NH heat wave frequency anomalies.We develop a multiple linear regression model to reconstruct NH heat wave frequencies based on the air-sea background factors of these three leading modes,including their spatial distributions and variance contributions.The model aligns well with observed heat wave frequencies and extreme high temperature events,reinforcing the significant impact of multi-scale oceanic and atmospheric signal on NH heat wave anomalies.When the absolute temperature thresholds are increased to 37 ℃ and 40 ℃,the leading modes display similar spatial patterns,suggesting that the identified oceanic and atmospheric drivers remain influential.Analysis based on relative temperature thresholds show consistent results,although variability at high latitudes exhibits a distinct contribution.Additional natural variability components,potentially linked to sea ice,snow cover,and soil moisture,warrant further investigation.

Abstract:The Madden-Julian Oscillation (MJO) is the dominant mode of intraseasonal variability in the tropics and has been widely studied since its discovery by Madden and Julian in 1971.The tropical oceans are a critical source of moisture,and positive sea surface temperature (SST) anomalies enhance heating and moisture fluxes,which facilitate the initialization and propagation of the MJO.Observations reveal a significant warming trend in the tropical Indian Ocean and western Pacific (TWIP) since 1979,associated with anthropogenic greenhouse gas forcing.However,previous studies indicate that current climate models struggle to simulate MJO variability accurately,underscoring the challenges in understanding its role in weather and the climate systems.This study examines the observed impacts of SST warming in the TWIP on the MJO,focusing on changes in its characteristics and their influence on winter precipitation in China from 1979 to 2012.South China,where precipitation is heaviest,has experienced a significant increase in rainfall since 1979,making it vital to investigate the contribution of MJO changes to this trend.The analysis reveals that SST warming in the TIWP intensifies heating and moisture fluxes,leading to an increase in MJO amplitude across all phases,with a statistical significant rise in phase 6.Vertical water vapor distribution changes result in an increased frequency of MJO phases,particularly phase 5,facilitating eastward propagation from phase 4 to phase 5 and prolonging phase 5 activity.These MJO changes are linked to enhanced subsidence in South China,associated with local Hadley cell dynamics,resulting in more negative sub seasonal precipitation anomalies during MJO phases 5 and 6 in recent decades.The findings provide observational evidence of the significant influence of SST warming on MJO activity and its subsequent impacts on regional precipitation patterns.The results highlight that MJO phase frequency changes reflect shifts in preferred regions of activity and structural adjustments.Future research should employ state-of-the-art GCMs to explore the influence of SST warming on MJO in greater detail.This study focuses on climatological changes in MJO under TWIP basin-wide warming.However,variations in MJO structure and their differential impacts on weather systems,warrant further diagnostic analysis.

Abstract:In the context of global warming,future climate projections have become a major research focus.The increasing greenhouse gas (GHG) emissions in the CMIP6 scenarios-SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5 are accompanied by rising global mean surface air temperatures.However,the potential changes in the seasonal cycle of atmospheric mass,particularly the annual range,remain an open scientific question.This study analyzes CMIP6 data from 2015 to 2100 to examine the seasonal cycle characteristics of atmospheric mass under four GHG emission scenarios compared to historical runs.The study also examines the spatial distribution of surface air pressure and geopotential height in February and July,interpreted through temperature and wind fields.The results reveal that while the seasonal variations of global and hemispheric average atmospheric mass in the different scenarios resembles those in historical simulations,the annual range of hemispheric air fluctuates rather than increasing uniformly with higher GHG emissions.Specifically,the annual range decreases in SSP1-2.6 and SSP3-7.0,while it increases in SSP2-4.5 and SSP5-8.5.Globally,the annual range of atmospheric mass increases steadily with higher GHG emissions.Surface air pressure and geopotential height distributions in future scenarios align with reanalysis data,but in the Northern Hemisphere,a phase transition emerges,linked to the land-sea distribution dominated by Eurasia,the North Pacific,and the North Atlantic.In contrast,the Southern Hemisphere phase transitions occur along the latitudinal zones.The alternating positive and negative values in the February-July difference suggest regionally varying trends and amplitudes of change in surface air pressure and wind fields due to GHGs.These regional differences contribute to variations in the annual range of surface air pressure across the hemispheres.With increasing GHG emissions,the Northern and Southern Hemispheres respond differently across seasons,and this variation is evident not only in the lower troposphere but also at higher altitudes,including the stratosphere.The Arctic and Antarctic stratospheric vortices show nonlinear changes in intensity,weakening and strengthening in cycles as GHG emissions rise.These results suggest that the uneven distribution of GHGs in the atmosphere leads to variable temperature responses at different altitudes,altering the geopotential potential height field,layer thicknesses,and,consequently,surface air pressure.This highlights the region-specific response of atmospheric mass to increasing GHGs.The CMIP6 simulations provide valuable insights into atmospheric circulation changes under the conservation of atmospheric mass and their impacts on weather and climate in a warming world.Additionally,the nonlinear effects of human activities on water vapor mass and the atmospheric water-holding capacity deserve further exploration,particularly regarding their influence on the annual range of atmospheric mass.It remains uncertain whether transient and stable warming scenarios will lead to different outcomes in air mass changes.

Abstract:The Yangtze River basin(YRB) is a key region for the occurrence of extreme high temperatures (EHT),which has significant impacts on both human society and ecosystems.The number of EHT days (EHTD) in the YRB exhibits notable interannual variability.Using sea surface temperature (SST) data from the Hadley Center,daily maximum temperature datasets from CN05.1,and reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR),this study investigates the relationship between summer North Atlantic SST anomalies (SSTA) and EHTD in the YRB through singular value decomposition (SVD) analysis.Results reveal that the first SVD mode demonstrates a strong relationship between North Atlantic Dipole (NAD) pattern (characterized by positive (negative) SSTAs in the midlatitude and negative (positive) SSTAS in the subtropical North Atlantic) and EHTD in the YRB,accounting for 63.43％ of the total squared covariance.The correlation coefficient between the time series of the first SVD mode for NAD SSTA and EHTD is 0.51,statistically significant at the 99％ confidence level.An interdecadal shift in the relationship between NAD SSTA and EHTD in the YRB occurred in the late 1980s.From 1961—1988,NAD SSTA showed a strong connection with EHTD in the YRB.However,after the late 1980s,this relationship weakened.Further analysis suggests that the interdecadal changes in the NAD SSTA-EHTD relationship are primarily driven by differences in atmospheric circulation anomalies over Eurasia,triggered by NAD SSTA.Before the late 1980s,positive (negative) NAD SSTA induced an atmospheric teleconnection pattern extending from the North Atlantic to East Asia in the middle and upper troposphere,featuring two positive (negative) geopotential height anomaly centers over the Ural Mountains and East Asia,and two negative (positive) centers over Greenland and Lake Baikal.This teleconnection pattern,linked to Rossby wave energy propagation,led to an anticyclone/cyclone circulation anomaly over East Asia,inducing anomalous descending/ascending motion over the YRB,which in turn provided favorable/unfavorable thermal conditions for EHT occurrences.The East Asian anticyclone/cyclone circulation anomaly had a significant influence on EHTD in the YRB.However,after the late 1980s,the aforementioned teleconnection pattern excited by NAD SSTA weakened,reducing the East Asian anticyclone/cyclone circulation anomaly and weakening the NAD SSTA-EHTD relationship.The differences in atmospheric circulation anomalies between the two periods are closely related to the reduced amplitude of NAD SSTA.The reasons behind these weakening of NAD SSTA amplitude remain to be explored.These findings offer valuable insights for understanding and predicting EHTD in the YRB.

Abstract:Hunan Province,located in southern China,experiences frequent and intense precipitation,especially during the rainy season.Recent years,have seen an increase in extreme precipitation events,characterized by significant diurnal variation,which complicates forecasting efforts.Over the past decade,China has developed a regional network of high-resolution,fully-automated weather stations,enhancing the study the study short-duration heavy rainfall.This study utilizes hourly precipitation data from 1 599 automatic weather stations across Hunan Province,collected from 2012 to 2021,to analyze the characteristics of hourly extreme precipitation during the rainy season (April—September).The study also examines the relationship between hourly extreme precipitation events and 12-hour rainstorms,focusing on their statistical characteristics and the contribution of hourly extreme events to overall rainstorm totals.This quantitative analysis aims to reveal the intrinsic connections between these events and provide a technical foundation for improving nowcasting and early warning systems.The 99.9％ percentile was selected as the threshold for defining extreme hourly precipitation in Hunan.Results show that the spatial distributions of frequency and intensity of extreme precipitation events are similar,with high-frequency areas concentrated in the Xuefeng Mountains,the southern Nanling Mountains,and the Dongting Lake area,with maximum values in southern Hunan.Complex terrain and underlying surfaces significantly enhance hourly precipitation intensity.The annual frequency of extreme hourly events in the rainy season exhibits wavelike growth,peaking in 2021 and reaching a low in 2013,where 2021 recorded nearly 90％ more events than in 2013.Extreme precipitation events are most common from May to August,peaking in June.Diurnally,extreme precipitation follows a bimodal pattern,peaking at 18:00 and 07:00 BST,with a rapid increase in the afternoon,a peak in the evening,a gradual decline overnight,and a secondary peak in the early morning.The spatial distribution of 12-hour rainstorm is similar to that of hourly extreme events,with an average annual frequency of 2 490 occurrences at night and 2 039 during the day,indicating a higher nighttime frequency.High-frequency areas for 12-hour rainstorms are found primarily in western Hunan,along the Xuefeng and Wuling Mountain ranges,with isolated high-frequency locations in the eastern and southern regions,while lower frequencies are observed between 25.5°N and 26.5°N.Monthly and daily variations in 12-hour rainstorm frequency show distinct regional patterns,with high-frequency zones varying from May to September and the Dongting Lake region experiencing the highest frequency in June and July.Daytime hourly extreme precipitation contributes significantly more to 12-hour rainstorms than nighttime events:in the daytime,41％ of stations report that hourly extreme events contribute 70％—90％ of 12-hour rainstorm totals,while this contribution rate is only 25％ at night.High-contribution regions are primarily concentrated in southern Hunan.

Abstract:Vapor pressure deficit (VPD) is a critical meteorological variable with significant implications for hydrology,ecology,and climate change.Numerous studies have demonstrated that VPD,a key indicator of atmospheric dryness,has undergone substantial changes in response to global climate warming.Investigating the spatiotemporal variations of VPD across different regions of China is essential for advancing climate change research and supporting agricultural and pastoral practices.While regional-scale VPD studies have been conducted by domestic scholars,comparative analysis of VPD changes at the national scale remain limited.This study uses daily mean air temperature and relative humidity data from 772 meteorological stations across China,applying Tetens' empirical formula to calculate VPD.Through co-Kriging interpolation,Mann-Kendall trend tests,and correlation and partial correlation analyses,the spatiotemporal variability and trends of VPD across different time scales and geographical regions from 1961 to 2015 were examined.Additionally,the study explores the relationships between VPD,temperature,and relative humidity,identifying the dominant factors influencing VPD changes over different periods.The results indicate significant spatiotemporal variation in VPD across China from 1961 to 2015,with higher values in summer and lower values in winter.Spatially,high annual average VPD values were concentrated in the arid and semi-arid regions of Northwest China,while lower values were found in Northeast China,the Qinghai-Xizang Plateau,and parts of the southern region.The annual average VPD across China ranged from 0.127 kPa to 1.547 kPa,with an overall average of 0.526 kPa.Most regions showed an increasing VPD trend over time.Between 1961 and 1999,57％ stations exhibited increasing VPD trends,and this proportion rose to 67％ between 2000 and 2015,with the year 2000 identified as a mutation point.The average VPD increase accelerated from 1.519 6 hPa/(10 a) to 7.074 3 hPa/(10 a) after 2000.Correlation and partial correlation analyses revealed that VPD was significantly positively correlated with temperature and negatively correlated with relative humidity in all regions.Maximum temperature was the dominant factor driving VPD changes in southern and northern China,while average temperature and relative humidity played a greater role in the Northwest and Qinghai-Xizang regions.These dominant factors remained consistent before and after the mutation point.While the primary factors influencing VPD changes were explored,further investigation is needed to assess the impact of other potential factors such as topography,vegetation cover,and broader climatic conditions.This study provides insights into the characteristics and drivers of VPD changes across seasons and regions,offering a scientific foundation for climate-ecological and vegetation modeling studies and valuable guidance for policy-makers and relevant stakeholders.

Abstract:The near-surface air temperature lapse rate (NTLR) is a crucial yet complex meteorological parameter that determines the rate at which air temperature decreases with increasing altitude.A uniform lapse rate across the troposphere cannot adequately capture the spatiotemporal variability of NTLR.Given the significant influences of topography,geographical location,seasonal cycles,diurnal variations,and human activities on NTLR,this study highlights the need for a spatiotemporally specific approach to accurately characterize these variations.The study has two primary objectives:first,to analyze the spatial and temporal variability of NTLR across different regions of China,and second,to assess the impact of these variations on the correction of temperature forecast biases in the NCEP model.According to comprehensive physical regionalization,China is divided into 33 sub-regions.Utilizing a dataset of hourly temperature observations from 2 427 national-level meteorological stations in China,covering the period from March 1,2013,to February 28,2022,the study calculates regional and seasonal NTLR values for both day and night using a ternary linear regression model.This model accounts for altitude,latitude,and longitude,providing a detailed representation of the NTLR’s relationship with geographical factors.The calculated NTLR values are then applied to correct elevation-based biases in the NCEP temperature forecasts from March 1,2022,to February 28,2023.The results indicate that:1) Significant spatiotemporal variations in NTLR exist across China,with differences occurring even within the same sub-region.The annual mean NTLR for China is 0.57 ℃/(100 m),with nighttime values slightly higher than daytime values.2) Seasonal and diurnal variations in NTLR are substantial,with the highest rates observed in summer (average 0.63 ℃/(100 m)) and the lowest in winter (average 0.47 ℃/(100 m)).The maximum day-night differences in NTLR are 0.22,0.29,0.28,and 0.50 ℃/(100 m) for spring,summer,autumn,and winter,respectively.3) Applying the statistically derived NTLR to correct elevation-based biases in the NCEP temperature forecasts reduces the mean absolute error (MAE) across China for all forecast periods up to 360 h,with an average reduction of 0.56 ℃ and a maximum reduction of 1.20 ℃.The correction is most effective in summer and during the daytime across most sub-regions.However,in some areas,such as the central and northern Qinghai-Xizang Plateau,applying NTLR for bias correction has led to reduced forecast accuracy,indicating that NTLR application must be adapted to local conditions.Future research should explore the impact of varying altitude ranges,slope aspects,and other factors on NTLR within the context of comprehensive physical regionalization.

Abstract:Rapid urbanization in China has exacerbated atmospheric pollution, particularly in urban areas.Urban lakes played a crucial role in moderating local climate and air quality by influencing atmospheric boundary layer circulation and pollutant transport mechanisms.However, previous studies have relied primarily on mesoscale models, where the influence of additional coupled mechanisms may impact the accuracy of sensitivity results.To address this, we utilized a conceptual urban land surface model to conduct suburban-scale sensitivity experiments, isolating the primary factors and mechanisms by which urban lakes influence the distribution and transport of near-surface pollutants.Focusing on the diurnal patterns of pollutant mass concentrations at lakeside and non-lakeside urban stations in Nanjing during the summer, we found notable differences in pollutant behavior.For NO₂, a ground-emitted pollutant, lakeside stations recorded a daytime average concentration of (1.64 ±0.29) μg·m^-3 higher than non-lakeside, while nighttime concentrations were (0.51 ±1.39) μg·m^-3 lower.In contrast, O₃, which forms at mid-and upper-boundary layers, exhibited lower daytime concentrations by (9.57 ±2.19) μg·m^-3 at lakeside stations, with nighttime levels (1.24 ±4.68) μg·m^-3 higher.No significant differences were found for PM_2.5 concentrations.Using a two-dimensional land surface model, we conducted sensitivity experiments to examine the effects of lake presence and lake-to-urban land distribution under different emission scenarios.Simulations indicated that the model accurately reproduced key temperature and pollutant mass concentrations patterns, comparable to more complex mesoscale model results.Thermal property differences between lake and urban land surfaces significantly impacted low-level atmospheric circulation and vertical stability, altering pollutant diffusion and transport.Daytime thermal stability and limited vertical diffusion over lakeside areas led to a higher concentrations of surface-emitted pollutants near lakes, while concentrations of pollutants formed at mid-levels were lower compared to non-lakeside areas;this pattern reversed at night.Simulation outcomes aligned well with observed data trends and were qualitatively consistent with WRF-Chem results.While emphasizing rigorous monitoring and data processing methods, spatial heterogeneity of urban structures and emissions, as well as observational data limitations, introduced some uncertainties.Future research should incorporate more extensive data from diverse urban regions to better generalize these patterns and strengthen the theoretical foundation for air quality management in urban lakeside environments.

Abstract:During the winter season in southern China,freezing rain and ice accretion often lead to substantial socio-economic impacts,including power interruption,tree damage,traffic disruptions,and risk to human safety.Scientific research into freezing rain and ice accretion processes,especially their formation mechanism,is critical for improving the forecast accuracy and mitigating associated hazards.This study presents a detailed numerical investigation of the freezing rain and ice accretion event that occurred in Hubei Province in the winter of 2010.The study utilized the European Centre for Medium-range Weather Forecasts (ECMWF) ERA-Interim reanalysis dataset (0.75°×0.75° spatial resolution,6 hours temporal resolution) as the initial field,in combining with the version v3.7.1 of the weather research and forecasting (WRF) model.Results reveals that the cloud microphysical structure and precipitation formation mechanisms remains consistent across different stages of the ice accretion process,with ice accretion predominantly forming through a supercooled warm rain process.The primary growth mechanism for rainwater in the cloud is the auto-conversion of cloud water to rain and the coalescence of rain with cloud water.Notably,the coalescence of rain with supercooled cloud water plays a more significant role in increasing rainwater mixing ratios.By analyzing the meteorological field and cloud microphysical data from the WRF simulation,and integrate them with the Jones Ice Thickness Prediction Model,the study captures the temporal evolution of ice thickness during the freezing rain event.The Jones model,which utilized readily available physical quantities such as wind speed,precipitation amount,and liquid water content,effectively simulate the trend of ice thickness overtime.A comparative analysis with observed datasets confirms that the model reasonably predict the trend of ice accretion thickness during the event.Meteorological factors,such as air temperature and precipitation intensity,significantly influence ice thickness.For instance,lower air temperature accelerate ice accretion growth,while increased precipitation intensity contributes to rapid ice accumulation.The in-depth analysis of influencing factors provides a robust numerical simulation framework for improving early warnings of freezing rain and ice accretion events.Findings of this study provide a relevant analysis of the physical mechanisms,simulation outcomes,and enhance forecasting capabilities for freezing rain and ice accretion processes.Besides,the results may contribute to advancing forecast accuracy,mitigating socio-economic impacts,and bolstering disaster prevention and response strategies.

Abstract:The traditional dichotomous contingency table test, which evaluates the objective performance of numerical weather prediction (NWP) based on the point-to-point matching between forecasted and observed events, has notable limitation when applied to high-resolution NWP or convection-allowing models (CAM). The neighborhood method addresses these limitations by relaxing the grid scale matching constraints between forecasted and observed events, making it particularly valuable for evaluating high-resolution numerical weather forecasts and the post-processing of objective probability forecasts. This paper systematically reviews the key applications of the neighborhood method in weather forecasting, focusing on two key aspects: one is the verification of high-resolution numerical models using neighborhood method; and other is the neighborhood probability or neighborhood probability of ensemble forecasts. First, the study outlines the verification frameworks of two neighborhood methods,“one-to-many” and “many-to-many”, and discusses the data processing techniques associated with the neighborhood method, alongside the physical interpretation of common scoring matrices such as FBS (fractions brier score) and FSS (fractions skill score). It is concluded that, in addition to traditional dichotomous contingency table-based verification metrics, the neighborhood method facilitates comparisons of forecast performance across multiple spatial and temporal scales. This enables the derivation of diagnostic metrics for NWP forecast performance based on scale changes, providing unique advantages. Second, it summarizes the fundamental concepts and statistical meaning of the grid scale neighborhood probability and the neighborhood probability at scales larger than the grid. Discussion focuses on expounding the algorithm workflow and internal meaning of neighborhood ensemble probability (NEP) forecast and neighborhood maximum ensemble probability (NMEP) forecast derived from ensemble forecasts. Third, by examining typical application cases, it analyzes the advantages, disadvantages and applicability of the neighborhood method and neighborhood ensemble probability. Results show that both NEP and NMEP enhance precipitation forecast scores. NEP performs better for large-scale and systematic precipitation forecasts, whereas NMEP is more effective for convective and extreme precipitation events. However, the selection of an appropriate neighborhood radius remains a critical technical challenge, as it is influenced by variations in underlying surface conditions and the optimal neighborhood scales of different NWP products. Finally, the paper discusses future directions for the application of the neighborhood method in weather forecasting. Promising areas of research and application include integrating neighborhood ensemble probability with the temporal dimension, developing metrics for the rare-event ensemble neighborhood probability, and exploring synergies between the neighborhood method and artificial intelligence. These directions hold significant potential for advancing the utility and impact of the neighborhood method in weather forecasting.

Abstract:Carbon dioxide (CO₂) is a primary greenhouse gas,and its rising atmospheric concentration is a critical driver of climate change,contributing to extreme weather,rising sea levels,and ecosystem alterations.Remote sensing via carbon satellites provides a powerful approach for large-scale,precise CO₂ monitoring;however,challenges with spatial and temporal sparsity limit the accuracy and continuity of CO₂ concentration (XCO₂) estimates,particularly across large regions like China's Yangtze River delta.To address these limitations,this study introduces the Space-Time Soft Attention Network (ST-SAN),a novel model designed to enhance the spatiotemporal resolution of XCO₂ estimates derived from carbon satellite data.The model leverages multi-source datasets (including human activity,meteorological,and vegetation) alongside carbon satellite observations,achieving a seamless XCO₂ dataset with a 0.05° spatial resolution,thus providing a detailed view of regional CO₂ dynamics.Training the ST-SAN model on data from 2016 to 2020,the methodology employs soft attention mechanisms to prioritize relevant features across spatial and temporal dimensions,enabling more accurate XCO₂ predictions.The model's effectiveness was rigorously evaluated by comparing reconstructed XCO₂ data with observations from the Orbiting Carbon Observatory-2 and ground-based monitoring stations,demonstrating high consistency and reliability.By integrating diverse datasets,the ST-SAN model effectively addresses the sparsity issues in satellite observations,enhancing predictive performance and offering a comprehensive framework for high-resolution CO₂ estimation.These findings underscore the potential of advanced machine learning techniques to improve atmospheric monitoring and provide critical insights for climate mitigation efforts.Future research could refine this model with additional data sources,extend its applicability to varied regions,and explore long-term CO₂ trends to better understand the influences of human activity and natural processes on greenhouse gas emissions.The study not only demonstrates the feasibility of high-resolution XCO₂ estimation but also establishes a foundation for more accurate climate assessments and informed environmental policy.

Abstract:In recent years,global warming has intensified extreme weather and climate events,significantly impacting urban areas.Shanghai,located in the eastern coastal region of China,is increasingly vulnerable to weather-related disasters.As a megacity,its urban operations are heavily influenced by these events,particularly gales,which pose severe threats to both infrastructure and public safety.Despite their impact,research on gales in Shanghai has been limited by incomplete and inconsistent disaster data.Since 2007,the Shanghai Emergency Response and Coordination Center has shared weather-related alarm data with the Shanghai Center for Meteorological Disaster Prevention Technology.This data includes detailed information on the timing,location,and nature of disasters,enabling a more thorough analysis of the impacts of weather events on the city.Using hourly maximum wind speed data from Shanghai's automatic stations and real-time gale disaster data provided by the Shanghai Emergency Response Coordination Center from 2008 to 2019,this study analyzes the spatiotemporal distributions of urban gales and their correlations with disasters.The findings are as follows:1) Gales and disasters exhibit significant seasonal variations,with force 8 gale days,maximum wind speeds,and disaster occurrences all peaking in summer.Summer gale days account for 41％ of the year,and gale-related disasters constitute over 80％ of the total.Inter-annual fluctuations in disasters are pronounced,primarily influenced by extreme weather events.2) Differences in gales and disasters are evident between urban and suburban areas:Ⅰ) Wind speeds are notably lower in downtown regions due to urbanization,while Chongming Island,Hengsha Island,and coastal areas southeast of Pudong experience significantly higher wind speeds due to funnel-shaped terrain effects.Ⅱ) The density of disaster cases is highest in the central city (up to 37 cases／km²) and remains relatively high in regional centers and sub-centers,with the number of disasters positively correlated with population density and GDP.III) Housing-related damages are most prominent in downtown areas and their surroundings,while damages to trees and power lines are significant in Chongming District,and vehicle-related damages are prevalent in Jiading District and Songjiang District.3) Gale disasters in Shanghai can be categorized into four types:Jiang-Huai cyclone gales,thunderstorm gales,tropical cyclone gales,and cold air gales.Thunderstorm gales are the most frequent,while tropical cyclone gales are the most severe.Jiang-Huai cyclone gales impact a broad area of the city but are infrequent,and cold air gales are the least severe.4) Trees and vehicles are the most common disaster-affected entities in strong wind events,followed by power lines and rain shelters.Gale disasters often trigger chain reactions.To effectively prevent and mitigate gale-related disasters,it is crucial for the city to develop detailed emergency response plans and enhance cooperation among management departments.Future research should focus on establishing thresholds for gale disasters and creating risk warning products,thereby advancing the city's disaster management capabilities to a more sophisticated and efficient level.