To investigate the "South-High North-Low" (SHNL) spatial distribution of aerosol pollution in the Beijing area, this study analyzes the spatial and temporal characteristics of aerosol and influencing mechanisms using hourly PM2.5 concentrations (diameter <= 2.5 mu m) data collected from 33 air quality stations in Beijing from 2015 to 2019. PM2.5 concentrations in the Beijing plain generally decreased, but the SHNL pattern remained prominent, with PM2.5 emission sources in Beijing and its surroundings showing a "south-strong, north-weak" distribution as an essential prerequisite. Between southern and northern regions, the maximum annual aerosol concentration difference (ACD) was 16.0 mu g m- 3, and the maximum seasonal ACD occurred in winter (24.1 mu g m-3) when the SHNL pattern was most prevalent. Higher Aerosol Concentration in Southern region (HACIS) is the dominant pollution type. Two distinct types of pollution events were analyzed. The HACIS type pollution event originated from synergistic effect of weak southerly and strong northerly winds. The HACIN (Higher Aerosol Concentration in Northern region) event originated from synergistic effect of stronger southerly and weaker northerly winds. The Regional Air-pollutant Distribution Regulator (RADR), defined as a local endogenous wind system (including mountain-plain circulation, urban heat island circulation, and sea-land breezes) and their interactions, plays a key regulatory role in shaping pollution distribution. This study aims to lay a preliminary foundation for understanding Beijing's aerosol pollution dynamics and may be of some relevance for developing targeted pollution control strategies.

heat storage (Q(s)) is a critical contributor to the urban heat island (UHI) effect, yet its spatiotemporal patterns and quantitative contribution remain poorly understood. Urban surface thermal properties directly influence Q(s), but accurately quantifying these properties at a regional scale remains a challenge. This study retrieves urban thermal inertia (TI) and hourly Q(s) based on heat conduction theory, using 24-h cycles of Himawari-8 land surface temperature (LST), in three urban agglomerations of China. The quantitative impact of Q(s) on near-surface air temperature (T-a) is also investigated at both city and local climate zones (LCZs) scales. With validation of Q(s) against flux tower data, the correlation coefficient can be up to 0.92 and root-mean-square error (RMSE) is 39 W/m(2). Results show that there is a significantly stronger correlation of Q(s) with Ta during nighttime (R-2 > 0.95) than daytime, confirming its dominant control in nocturnal UHI. To be specific, across LCZs, high-rise and open-built areas exhibited greater nocturnal Q(s) (and correspondingly higher Ta) compared to compact mid-lowrise neighborhoods. Notably, water bodies remain higher Qs (higher Ta) than vegetated surfaces. Entropy-based thermodynamic assessment suggests high-rise buildings exhibit higher but faster UHI change than low-rise. For future urban planning, we recommend prioritizing the heat mitigation of high-rise buildings and strategically planning blue space to balance its daytime cooling against nocturnal warming effects.

The urban neighborhood serves as the fundamental unit for fine-scale management of urban carbon emissions, playing a critical role in achieving urban carbon neutrality and sustainable development. Carbon source-sink data at the urban neighborhood scale exhibit significant spatial heterogeneity, and accurate estimation of CO2 fluxes helps to better understand the relationship between urban carbon processes and both anthropogenic and natural factors. This study employed multi-source data and the SUEWS urban land surface model to simulate CO2 fluxes at the neighborhood scale. High temporal (hourly) and spatial (20 m) resolution CO2 fluxes were obtained in the typical mixed-use areas surrounding the IAP and RCEES stations in Beijing. Carbon fluxes from traffic, buildings, human metabolism, soil, and vegetation were quantified for the years 2016, 2019, and 2020. The results show that the SUEWS model effectively captures the temporal and spatial dynamics of CO2 flux. The multi-year average CO2 flux at IAP and RCEES stations was 28.17 and 12.87 kg CO2/m2 per year, respectively. Traffic accounted for the largest share of CO2 emissions, contributing more than 50%, followed by emissions from buildings and human metabolism. The study also evaluated the potential for carbon reduction in urban neighborhoods under future low-carbon policies. Under the moderate emission scenario SSP2-4.5, along with the implementation of strong policy measures, including 80% rooftop greening and electric vehicle adoption, carbon emissions in urban neighborhoods could be reduced by approximately 60%. This study provides essential data and technical support for urban CO2 reduction through fine-scale CO2 flux calculations.

Top‐down methods commonly use atmospheric CO 2 concentration observations to constrain carbon source and sinks. Despite the increase in spaceborne and ground‐based concentration measurements, atmospheric inversions are usually limited by uncertainties in chemical transport models (CTMs) when relating fluxes to observed CO 2 mole fractions. CO 2 eddy covariance (EC) flux measurements have been widely used to directly measure CO 2 fluxes over various ecosystems, but they have rarely been used as constraints in top‐down estimations. In this study, we focused on the development of a novel fluxes assimilation scheme through direct flux observations within an Ensemble Square Root Filter assimilation framework. The assimilation scheme avoided some of complexities of concentration observation assimilations. The methodology was primarily applied to typical regions in west China, taking advantage of eight long‐term ecosystem EC sites. Moreover, four sets of assimilation experiments were designed to quantify the impacts of observational constraints by flux and concentration measurements. Generally, results indicate that the monthly and hourly statistics of the a posteriori fluxes constrained by flux observations agreed well with flux measurements, demonstrating reasonable performance in seasonal and diurnal variations. Specifically, assimilation results demonstrated the advantage of a posteriori estimates inferred from flux measurements during growing season, as compared to results inferred from concentrations, while some limitation still exists in monthly budget estimates. Nevertheless, it is important to note that current results are only a mathematical optimum. CO 2 biospheric fluxes can be estimated more reliably and robustly at the regional scale given considerably more flux observations for efficient constraint.

The Surface Urban Energy and Water Balance Scheme (SUEWS) is a widely used model in urban climate modelling, addressing numerous urban climatic challenges. Due to the complexity and wide range of physical processes being modelled, properly setting up the model for a specific site can be difficult. A user may have extensive knowledge of building energy but limited understanding of phenology, leading to the use of vegetation parameters that are not suited for the modelled domain.Urban areas are heterogeneous and complex environments characterised by high variability in geometry, land use, surface materials, vegetation, and anthropogenic activity. Many NWP models utilise a grid-based approach to model energy fluxes, even though grids do not necessarily reflect the actual structure of cities. Within these grids, there is significant variability in building height, age, function, form, and materials, making proper parameterisation challenging.To make it easier for users to choose relevant parameters, the new SUEWS property database has been developed. This database provides evidence-based parameter entries for different geographical contexts. Located within Urban Multi-scale Environmental Predictor (UMEP) toolbox in QGIS, it allows users to investigate and add parameters. A new SUEWS prepare QGIS plugin has also been developed, utilising urban typologies that represent certain properties for specific urban neighbourhoods. These typologies enable the swift calculation of urban characteristics and allow for the aggregation of parameters within SUEWS grids. Users can easily create new typologies suited to their needs.To make the database more comprehensive, a call is being made to SUEWS users in the urban climate community to help update and fill the database with parameters from different parts of the world, building and vegetation types, traffic profiles, etc. By sharing our knowledge and parameters, we can improve SUEWS modelling for all users.

Urban heat storage (Qs) is an essential component of urban surface energy balance. Qs is the main factor for urban heat island (UHI) at nighttime. The quantitative contribution of Qs to UHI is still unclear, due to the lack of a spatio-temporal continuous Qs dataset. In this study, firstly, we developed an urban surface thermal inertia model using hourly LST of Himawari-8. Secondly, the hourly Qs in three urban agglomerations in China was simulated by the heat diffusion equation and Fourier’s law for heat conduction, using the simulated urban thermal inertia and Himawari-8 LST. Thirdly, the relationship between Qs and air temperature (Ta) was studied. Based on the in-situ observation, the accuracy of urban thermal inertial in this study was higher than other model, RMSE, MAE, R2 were improved from 4.65 K, 3.58 K and 0.88 to 1.86 K, 1.53 K and 0.97. In addition, Qs were validated by the observed Qs (from flux tower observation) in Beijing, Shanghai and Guangzhou, R2 could be up to 0.92. Results showed that, Qs was more consistent with Ta at nighttime than daytime, with R2 of 0.96 and 0.1, respectively. During nighttime, the high-rise building has higher Ta than low-rise building, due to higher Qs and release more energy than low-rise. In natural surfaces, water has larger Qs and higher Ta than dense trees. The loop (scatterplot of hourly Qs and Ta) shape were different at LCZs. Based on the loop area and slope, we found that high-rise building had higher UHI but varied quickly, however, low-rise UHI is lower but would last longer. The water surface in night is also heat source and has a longer time UHI. Therefore, the high-rise building and water surface are not conductive to alleviating the nighttime UHI.

A detailed analysis of a sea breeze front (SBF) that penetrated inland in the Beijing–Tianjin–Hebei urban agglomeration of China was conducted. We focused on the boundary layer structure, turbulence intensity, and fluxes before and after the SBF passed through two meteorological towers in the urban areas of Tianjin and Beijing, respectively. Significant changes in temperature, humidity, winds, CO 2 , and aerosol concentrations were observed as the SBF passed. Differences in these changes at the two towers mainly resulted from their distances from the ocean, boundary layer conditions, and background turbulences. As the SBF approached, a strong updraft appeared in the boundary layer, carrying near-surface aerosols aloft and forming the SBF head. This was followed by a broad downdraft, which destroyed the near-surface inversion layer and temporarily increased the surface air temperature at night. The feeder flow after the thermodynamic front was characterized by low-level jets horizontally, and downdrafts and occasional up-drafts vertically. Turbulence increased significantly during the SBF’s passage, causing an increase in the standard deviation of wind components in speed. The increase in turbulence was more pronounced in a stable boundary layer compared to that in a convective boundary layer. The passage of the SBF generated more mechanical turbulences, as indicated by increased friction velocity and turbulent kinetic energy (TKE). The shear term in the TKE budget equation increased more significantly than the buoyancy term. The atmosphere shifted to a forced convective state after the SBF’s passage, with near isotropic turbulences and uniform mixing and diffusion of aerosols. Sensible heat fluxes (latent heat and CO 2 fluxes) showed positive (negative) peaks after the SBF’s passage, primarily caused by horizontal and vertical transport of heat (water vapor and CO 2 ) during its passage. This study enhances understanding of boundary layer changes, turbulences, and fluxes during the passage of SBFs over urban areas.

Measurements of radiative and turbulent heat fluxes for 16 months in suburban Miyun with a mix of buildings and agriculture allows the changing role of these fluxes to be assessed. Daytime turbulent latent heat fluxes (QE) are largest in summer and smaller in winter, consistent with the net all-wave radiation (Q*), whereas the daytime sensible heat flux (QH) is greatest in spring but smallest in summer rather than in winter, as commonly observed in suburban areas. The results have larger seasonal variability in energy partitioning compared to previous suburban studies. Daytime energy partitioning is between 0.15–0.57 for QH/Q* (mean summer = 0.16; winter = 0.46), 0.06–0.56 for QE/Q* (mean summer = 0.52; winter = 0.10), and 0.26–7.40 for QH/QE (mean summer = 0.32; winter = 4.60). Compared to the literature for suburban areas, these are amongst the lowest and highest values. Results indicate that precipitation, irrigation, vegetation growth activity, and land use and land cover all play critical roles in the energy partitioning. These results will help to enhance our understanding of surface–atmosphere energy exchanges over cities and are critical to improving and evaluating urban canopy models needed to support integrated urban services that include urban planning to mitigate the adverse effects of urban climate change.

Coronavirus disease 2019 (COVID-19) is seriously threatening and altering human society. Although prevention and control measures play an important role in preventing the transmission of severe acute respiratory syndrome coronavirus, signals of climate impact can still be detected globally. In this paper, the data of 265 cities in China were analyzed. The results show that the correlations between COVID-19 and air quality index (AQI) and PM2.5 concentration were very weak and that the correlations between COVID-19 and meteorological factors were significantly different in different climate backgrounds. So, a fixed model is not enough to describe the correlations. Overall, high humidity, low wind speed, and relatively lower air temperature are conducive to the spread of COVID-19. The climate background suitable for the spread of COVID-19 in China is air temperature 0~15°C, specific humidity <3 g kg−1, and wind speed <3 m s−1. The Granger causality test shows that there is a causal relationship between daily average air temperature and the number of COVID-19 confirmed cases in some cities of China, and air temperature is indicative of the number of confirmed cases the next day. However, this phenomenon is not universal due to regional climate differences.

Urban land surface models (ULSMs) are important tools for studying the climatic effects of human activity. Various kinds of ULSMs have been developed to simulate the land-atmosphere interactions in urban areas. Although the parameterization schemes of these models have become increasingly complicated, the influencing mechanisms and approaches of human-made land surface construction on urban land-atmosphere interactions still need further research. In this paper, some key parameters that are associated with urban land surface modeling were determined through a mechanistic study of the urban surface radiation budget and energy balance schemes in the integrated urban land model (IUM). These parameters are the surface albedo, three-dimensional fractional vegetation cover (FVC) and the correction factors for the net radiation absorption and surface roughness length. Then, these parameters were parameterized and optimized through a comparison with the observed flux data at the 325-m meteorology tower in downtown Beijing to improve the simulation ability of the IUM. The simulation results from the Noah land surface model coupled with the single-layer urban canopy model (Noah/SLUCM) were used and compared with the results from the IUM to demonstrate the better performance of the IUM model. The results indicate that after parameterizing and optimizing these key parameters, both the fluxes associated with the radiation budget and energy balance are substantially improved.

A comprehensive measurement of planetary boundary layer (PBL) meteorology was conducted at 140 and 280 m on a meteorological tower in Beijing, China, to quantify the effect of aerosols on radiation and its role in PBL development. The measured variables included four-component radiation, temperature, sensible heat flux (SH), and turbulent kinetic energy (TKE) at 140 and 280 m, as well as PBL height (PBLH). In this work, a method was developed to quantitatively estimate the effect of aerosols on radiation based on the PBLH and radiation at the two heights (140 and 280 m). The results confirmed that the weakened downward shortwave radiation (DSR) on hazy days could be attributed predominantly to increased aerosols, while for longwave radiation, aerosols only accounted for around one-third of the enhanced downward longwave radiation. The DSR decreased by 55.2 W m−2 on hazy days during noontime (1100–1400 local time). The weakened solar radiation decreased SH and TKE by enhancing atmospheric stability, and hence suppressed PBL development. Compared with clean days, the decreasing rates of DSR, SH, TKE, and PBLH were 11.4%, 33.6%, 73.8%, and 53.4%, respectively. These observations collectively suggest that aerosol radiative forcing on the PBL is exaggerated by a complex chain of interactions among thermodynamic, dynamic, and radiative processes. These findings shed new light on our understanding of the complex relationship between aerosol and the PBL.

The urban heat storage flux, $Q_{\mathrm {S}}$ , is one of the main drivers of the nocturnal urban heat island effect. However, the complex 3-D building structure makes observations and simulations of $Q_{\mathrm {S }}$ difficult. This study observes the 3-D surface radiant temperature ( $T_{\mathrm {s}}$ ) of a building in Beijing, China. The element surface temperature method (ESTM) and the half-order (HO) method are compared for $Q_{\mathrm {S}}$ simulation using $T_{\mathrm {s}}$ observations. The impact of building structure on $Q_{\mathrm {S}}$ and urban heat island intensity (UHII) are also studied. Results show the following. First, $Q_{\mathrm {S}}$ ’s simulated by ESTM and HO are nearly the same for walls. However, the HO method only needs one-layer exterior surface temperature, which has great potential for regional $\Delta Q_{\mathrm {S}}$ simulation by satellite remote sensing data. Second, during the daytime, $Q_{\mathrm {S}}$ ’s of each facet are significantly different from each other. The maximum observed difference of $Q_{\mathrm {S}}$ is up to 452 W/m 2 between the roof and north wall in May 2019. Third, complete $Q_{\mathrm {S}}$ ( $Q_{\mathrm {S, c}}$ ) is calculated by each facet $Q_{\mathrm {S}}$ and area fraction. The relationships between UHII and both 2-D $Q_{\mathrm {S}}$ (roof $Q_{\mathrm {S}}$ ) and 3-D $Q_{\mathrm {S }}(Q_{\mathrm {S, c}})$ are studied. $Q_{\mathrm {S}}$ is positively correlated with nocturnal UHII, and 3-D $Q_{\mathrm {S}}$ corresponds more closely to UHII with a larger Spearman’s coefficient ( $p < 0.05$ ). This study presents the effect of building structure on heat flux and could provide an insight for future $Q_{\mathrm {S}}$ and urban heat island (UHI) studies.

Abstract. Urban land surface model (ULSM) is an important tool to study the climatic effect of human activity. Now there are two main methods to parameterize the effects of human activity, the coupling method and the integrating method. For the coupled method, the urban canopy model (UCM) was developed and coupled with the land surface model for the natural land surfaces. For the integrated method, the urban land surface model was built directly based on the traditional land surface model. In this paper, the Noah Single Layer Urban Canopy Model (Noah/SLUCM) and the Integrated Urban land Model (IUM) were compared using the observed fluxes data at the 325-meter meteorology tower in Beijing. Through the comparison, the key factors and physical processes of the urban land surface model which have significant impact on the performance of ULSM were found out. The results indicate that the absorbed solar radiation of urban surface was reduced by the solar radiation scattering, the absorption of building roof and wall, and the shading effect of urban canopy and tall buildings. Urban surface roughness length and friction velocity are important in urban sensible heat flux simulation. Urban water balance and impervious surface evaporation (ISE) are important in urban latent heat flux simulation.

为了获取大气湍流和空间三维风场结构,利用3台同型号的测风激光雷达开展协同观测试验.(1)利用虚拟铁塔协同观测技术开展大气湍流探测,与香河102 m铁塔安装的三维超声风速仪观测结果做对比,32 m处高频(10 Hz)风速的相关系数高达0.92,平均误差为0.77 m/s,均方根误差为0.41 m/s;大气湍流强度(TKE)的相关系数高达0.99,平均误差为?0.02 m2/s2,均方根误差为0.08 m2/s2,并且协同观测的高频风速与三维超声风速仪的观测结果具有相同的频谱结构.(2)利用扫描协同观测技术开展三维风场探测,与铁塔上的常规测风设备相比,其90 m高度处的水平风速和风向的相关系数分别为0.92和0.93,平均误差为?0.41 m/s和0°,均方根误差为0.73 m/s和34°.相比于单台测风激光雷达,基于3台测风激光雷达协同观测技术具有一定的优势:不需要风场水平均匀的假设、探测精度更高等.但其对观测环境的要求较高:观测路径上不能有遮挡、观测必须协同等.在科研业务应用中,需要根据实际的观测需求合理制定观测方案.

Summertime (June–August 2015) radiative and turbulent heat fluxes were measured concurrently at two sites (urban and suburban) in Beijing. The urban site has slightly lower incoming and outgoing shortwave radiation, lower atmospheric transmissivity and a lower surface albedo than the suburban site. Both sites receive similar incoming longwave radiation. Although the suburban site had larger daytime outgoing longwave radiation (L↑), differences in the daily mean L↑ values are small, as the urban site has higher nocturnal L↑. Overall, both the midday and daily mean net all‐wave radiation (Q*) for the two sites are nearly equal. However, there are significant differences between the sites in the surface energy partitioning. The urban site has smaller turbulent sensible heat (Q H) (21–25% of Q* [midday–daily]) and latent heat (Q E) fluxes (21–45% of Q*). Whereas, the suburban proportions of Q* are Q H 32–32% and Q E 39–66%. The daily (midday) mean Bowen ratio (Q H/Q E) was 0.56 and 0.49 (0.98 and 0.83) for the urban and suburban sites, respectively. These values are low compared with other urban and suburban areas with similar or larger fractions of vegetated cover. Likely, these are caused by the widespread external water use for road cleaning/wetting, greenbelts, and air conditioners. Our suburban site has quite different land cover to most previous suburban studies as crop irrigation supplements rainfall. These results are important in enhancing our understanding of surface–atmosphere energy exchanges in Chinese cities and can aid the development and evaluation of urban climate models and inform urban planning strategies in the context of rapid global urbanization and climate change.

Urbanization has led to a significant urban heat island (UHI) effect in Beijing in recent years. At the same time, air pollution caused by a large number of fine particles significantly influences the atmospheric environment, urban climate, and human health. The distribution of fine particulate matter (PM2.5) concentration and its relationship with the UHI effect in the Beijing area are analyzed based on station-observed hourly data from 2012 to 2016. We conclude that, (1) in the last five years, the surface concentrations of PM2.5 averaged for urban and rural sites in and around Beijing are 63.2 and 40.7 µg m−3, respectively, with significant differences between urban and rural sites (ΔPM2.5) at the seasonal, monthly and daily scales observed; (2) there is a large correlation between ΔPM2.5 and the UHI intensity defined as the differences in the mean (ΔTave), minimum (ΔTmin), and maximum (ΔTmax) temperatures between urban and rural sites. The correlation between ΔPM2.5 and ΔTmin (ΔTmax) is the highest (lowest); (3) a Granger causality analysis further shows that ΔPM2.5 and ΔTmin are most correlated for a lag of 1–2 days, while the correlation between ΔPM2.5 and ΔTave is lower; there is no causal relationship between ΔPM2.5 and ΔTmax; (4) a case analysis shows that downwards shortwave radiation at the surface decreases with an increase in PM2.5 concentration, leading to a weaker UHI intensity during the daytime. During the night, the outgoing longwave radiation from the surface decreases due to the presence of daytime pollutants, the net effect of which is a slower cooling rate during the night in cities than in the suburbs, leading to a larger ΔTmin.

This article addresses the nexus of intense urbanization, building energy and air pollution, a topic minimally explored in the literature. The urban heat island effect on building energy demands for cooling and heating is investigated for Beijing through observations and modeling with a coupled Building Effect Parameterization-Building Energy Model and a single building energy model. The average urban heat island intensity in Beijing during summer is approximately 2.02 K, and in winter, this value reaches 3.41 K. The models used for the investigation are forced with observations from two meteorological towers, one located in downtown and the other in the outskirt. Model validation is conducted for environmental variables and for building energy demands against surface weather observations and actual electricity data, showing good agreement in all cases. Results for a 6-storey office building indicate that cooling energy use in the urban area is 36.53 Wm(-2) (30%) higher than the suburban area during summer, while heating energy use is 95.29 Wm(-2) (23%) lower than the suburbs during winter. Residential building shows similar results, with smaller differences in cooling and heating energy use, about 9.14 Wm(-2) (17%) and 92.71 Wm(-2) (20%), respectively. Analysis of clear and polluted winter days shows the impact-chain of air pollution - urban heat island - heating energy use. Heating energy demand is reduced in the urban area during polluted days, corresponding to an enhanced heat island, which may be attributed to a stronger inversion and a lower wind speed.

利用2012～2013年北京中央商务区(Central Business District,CBD)加密观测资料,分析CBD区域城市热岛(Urban Heat Island,UHI)强度日变化和空间变化特征及其影响因子.研究发现,CBD区域气温高于周边自动站气温,平均偏高0.64°C;CBD区域城市热岛强度呈现夜间强、白天弱的现象,中午甚至存在"城市冷岛"现象.季节平均UHI日变化表现为:在夜间,秋季最强,冬季次之,春季和夏季较弱;在白天,夏季最强,冬季次之,春季和秋季较弱.相对于晴朗无风天气,雾、雨、大风等天气对城市热岛有抑制作用,并结合小波分析结果发现,秋季城市热岛强度强于冬季是由于冬季雾、雨、大风等天气过程发生比例较高的缘故.CBD区域城市热岛空间变化特征研究发现,花园、学校等绿地有助于缓解城市热岛效应.雾日、雨日和大风日的CBD区域城市热岛强度空间变化标准差比晴朗无风日小.

A Met Office/Natural Environment Research Council Joint Weather and Climate Research Programme workshop brought together 50 key international scientists from the UK and international community to formulate the key requirements for an Urban Meteorological Research strategy. The workshop was jointly organised by University of Reading and the Met Office.