Explore the vast range of titles available.

In January 2013, February 2014, December 2015 and December 2016 to 10 January 2017, 12 persistent heavy aerosol pollution episodes (HPEs) occurred in Beijing, which received special attention from the public. During the HPEs, the precise cause of PM2.5 explosive growth (mass concentration at least doubled in several hours to 10 h) is uncertain. Here, we analyzed and estimated relative contributions of boundary-layer meteorological factors to such growth, using ground and vertical meteorological data. Beijing HPEs are generally characterized by the transport stage (TS), whose aerosol pollution formation is primarily caused by pollutants transported from the south of Beijing, and the cumulative stage (CS), in which the cumulative explosive growth of PM2.5 mass is dominated by stable atmospheric stratification characteristics of southerly slight or calm winds, near-ground anomalous inversion, and moisture accumulation. During the CSs, observed southerly weak winds facilitate local pollutant accumulation by minimizing horizontal pollutant diffusion. Established by TSs, elevated PM2.5 levels scatter more solar radiation back to space to reduce near-ground temperature, which very likely causes anomalous inversion. This surface cooling by PM2.5 decreases near-ground saturation vapor pressure and increases relative humidity significantly; the inversion subsequently reduces vertical turbulent diffusion and boundary-layer height to trap pollutants and accumulate water vapor. Appreciable near-ground moisture accumulation (relative humidity> 80 %) would further enhance aerosol hygroscopic growth and accelerate liquid-phase and heterogeneous reactions, in which incompletely quantified chemical mechanisms need more investigation. The positive meteorological feedback noted on PM2.5 mass explains over 70 % of cumulative explosive growth.

The explosive growth of PM2.5 mass usually results in extreme PM2.5 levels and severe haze pollution in eastern China, and is generally underestimated by current atmospheric chemistry models. Based on one such model, GRAPES_CUACE, three sensitivity experiments – a “background” experiment (EXP1), an “online aerosol feedback” experiment (EXP2), and an “80 % decrease in the turbulent diffusion coefficient of chemical tracers” experiment, based on EXP2 (EXP3) – were designed to study the contributions of the aerosol–radiation feedback (AF) and the decrease in the turbulent diffusion coefficient to the explosive growth of PM2.5 during a “red alert” heavy haze event in China's Jing–Jin–Ji (Beijing–Tianjin–Hebei) region. The results showed that the turbulent diffusion coefficient calculated by EXP1 was about 60–70 m-2 s-1 on a clear day and 30–35 m-2 s-1 on a haze day. This difference in the diffusion coefficient was not enough to distinguish between the unstable atmosphere on the clear day and the extremely stable atmosphere during the PM2.5 explosive growth stage. Furthermore, the inversion calculated by EXP1 was obviously weaker than the actual inversion from sounding observations on the haze day. This led to a 40 %–51 % underestimation of PM2.5 by EXP1; the AF decreased the diffusion coefficient by about 43 %–57 % during the PM2.5 explosive growth stage, which obviously strengthened the local inversion. In addition, the local inversion indicated by EXP2 was much closer to the sounding observations than that indicated by EXP1. This resulted in a 20 %–25 % reduction of PM2.5 negative errors in the model, with errors as low as -16 % to -11 % in EXP2. However, the inversion produced by EXP2 was still weaker than the actual observations, and the AF alone could not completely explain the PM2.5 underestimation. Based on EXP2, the 80 % decrease in the turbulent diffusion coefficient of chemical tracers in EXP3 resulted in near-zero turbulent diffusion, referred to as a “turbulent intermittence” atmospheric state, which subsequently resulted in a further 14 %–20 % reduction of the PM2.5 underestimation; moreover, the negative PM2.5 errors were reduced to -11 % to 2 %. The combined effects of the AF and the decrease in the turbulent diffusion coefficient explained over 79 % of the underestimation of the explosive growth of PM2.5 in this study. The results show that online calculation of the AF is essential for the prediction of PM2.5 explosive growth and peaks during severe haze in China's Jing–Jin–Ji region. Furthermore, an improvement in the planetary boundary layer scheme with respect to extremely stable atmospheric stratification is essential for a reasonable description of local “turbulent intermittence” and a more accurate prediction of PM2.5 explosive growth during severe haze in this region of China.

We present a suite of large-eddy simulations (LES) of a wind farm operating in conventionally neutral boundary layers. A fixed 1.6 GW wind farm is considered for 40 different atmospheric stratification conditions to investigate effects on wind-farm efficiency and blockage, as well as related gravity-wave excitation. A tuned Rayleigh damping layer and a wave-free fringe-region method are used to avoid spurious excitation of gravity waves, and a domain-size study is included to evaluate and minimize effects of artificial domain blockage. A fully neutral reference case is also considered, to distinguish between a case with hydrodynamic blockage only, and cases that include hydrostatic blockage induced by the air column above the boundary layer and the excitation of gravity waves therein. We discuss in detail the dependence of gravity-wave excitation, flow fields and wind-farm blockage on capping-inversion height, strength and free-atmosphere lapse rate. In all cases, an unfavourable pressure gradient is present in front of the farm, and a favourable pressure gradient in the farm, with hydrostatic contributions arising from gravity waves at least an order of magnitude larger than hydrodynamic effects. Using respectively non-local and wake efficiencies $\\eta _{nl}$ and $\\eta _{w}$, we observe a strong negative correlation between the unfavourable upstream pressure rise and $\\eta _{nl}$, and a strong positive correlation between the favourable pressure drop in the farm and $\\eta _{w}$. Using a simplified linear gravity-wave model, we formulate a simple scaling for the ratio $(1-\\eta _{nl})/\\eta _{w}$, which matches reasonably well with the LES results.

Rapid and simple estimation of the mass eruption rate (MER) from column height is essential for real‐time volcanic hazard management and reconstruction of past explosive eruptions. Using 134 eruptive events from the new Independent Volcanic Eruption Source Parameter Archive (IVESPA, v1.0), we explore empirical MER‐height relationships for four measures of column height: spreading level, sulfur dioxide height, and top height from direct observations and as reconstructed from deposits. These relationships show significant differences and highlight limitations of empirical models currently used in operational and research applications. The roles of atmospheric stratification, wind, and humidity remain challenging to detect across the wide range of eruptive conditions spanned in IVESPA, ultimately resulting in empirical relationships outperforming analytical models that account for atmospheric conditions. This finding highlights challenges in constraining the MER‐height relation using heterogeneous observations and empirical models, which reinforces the need for improved eruption source parameter data sets and physics‐based models. Plain Language Summary Explosive volcanic eruptions expel gas and tephra in the form of a volcanic column (or plume) that rises into the atmosphere. Two important metrics characterizing these eruptions are the maximum rise height and the eruptive intensity, that is, the rate at which material is emitted from the eruptive vent. Understanding the relationship between these parameters is critical for reconstructing past volcanic events and managing hazards during volcanic crises. In this study, we use a new database of well‐characterized eruptions to constrain simple relationships between column height and eruptive intensity. We distinguish four different measurements of column height: the maximum height reached by tephra from observations and from analysis of deposits, the height at which ash spreads in the atmosphere, and the height reached by volcanic sulfur gases. We show that each height category has a distinct relationship with the eruption intensity, enabling volcanologists and risk managers to use the relationship most appropriate to the measurements available to them. Despite the improved level of detail, our data set cannot resolve any systematic influence of atmospheric conditions such as wind and humidity on eruption column height, highlighting difficulties in measuring volcanic eruption characteristics and understanding their dynamics. Key Points We provide empirical scaling relationships between mass eruption rate (MER) and column height using a new database with 134 volcanic events We constrain bespoke relationships and their uncertainties for four height metrics to support ash dispersion forecasters and researchers We detect no clear atmospheric influence on scaling relationships, highlighting required improvements of scaling models and the database

A future projection of four extreme precipitation indices over the Indochina Peninsula and South China (INCSC) region with reference to the period 1958–2014 is conducted through the application of a multimodel ensemble approach and a rank-based weighting method. The weight of eachmodel from phase 6 of the Coupled Model Intercomparison Project (CMIP6) is calculated depending on its historical simulation skill. Then, the weighted and unweighted ensembles are used for future projections. The results show that all four extreme precipitation indices are expected to increase over the INCSC region, both in the middle (2041–60) and at the end (2081–2100) of the twenty-first century, under three Shared Socioeconomic Pathway (SSP) scenarios. The increases in total extreme precipitation (R95p), extreme precipitation days (R95d), and the fraction of total rainfall from events exceeding the extreme precipitation threshold (R95pT) in the Indochina Peninsula are more significant than those in South China. The occurrence of extreme rainfall events may become more frequent in the future over the INCSC region, since the probability that R95pT increases is larger than 0.7 in the whole INCSC region. A comparison between the weighted and unweighted ensemble means shows that the uncertainty over South China is almost always reduced after applying the weighted scheme to future probabilistic projection, while the reductions in uncertainty over the Indochina Peninsulamay depend on SSPs. Themore extreme precipitation over the INCSC region in the future may be related to the larger water vapor supply and the more unstable local atmospheric stratification.

Assessing change in daily maximum wind speed and its likely causes is crucial for many applications such as wind power generation and wind disaster risk governance. Multidecadal variability of observed near-surface daily maximum wind speed (DMWS) from 778 stations over China is analyzed for 1975–2016. A robust homogenization protocol using the R package Climatol was applied to the DMWS observations. The homogenized dataset displayed a significant (p < 0.05) declining trend of −0.038 m s−1 decade−1 for all China annually, with decreases in winter (−0.355 m s−1 decade−1, p < 0.05) and autumn (−0.108 m s−1 decade−1; p < 0.05) and increases in summer (+0.272 m s−1 decade−1, p < 0.05) along with a weak recovery in spring (+0.032 m s−1 decade−1; p > 0.10); that is, DMWS declined during the cold semester (October–March) and increased during the warm semester (April–September). Correlation analysis of the Arctic Oscillation, the Southern Oscillation, and the west Pacific modes exhibited significant correlation with DMWS variability, unveiling their complementarity in modulating DMWS. Further, we explored potential physical processes relating to the atmospheric circulation changes and their impacts on DMWS and found that 1) overall weakened horizontal airflow [large-scale mean horizontal pressure gradient (from −0.24 to +0.02 hPa decade−1) and geostrophic wind speed (from −0.6 to +0.6 m s−1 decade−1)], 2) widely decreased atmospheric verticalmomentum transport [atmospheric stratification thermal instability (from −3 to +1.5 decade−1) and vertical wind shear (from −0.4 to +0.2 m s−1 decade−1)], and 3) decreased extratropical cyclones frequency (from −0.3 to 0 month decade−1) are likely causes of DMWS change.

NCEP CFSR reanalysis 6-hourly fields from 1979 to 2015 were used to investigate the relationships of sea ice concentration (SIC), atmospheric stratification, surface roughness, and wind speed at 10-m height (W10) and 850-hPa level (W850). We found that in autumn (September–November), winter (December–February), and spring (March–May) a lower SIC favors less-stable stratification and a higher W10. In autumn, the decrease in SIC is strongest, and SIC has its strongest correlation with the atmospheric stratification, W10, and the ratio of W10 and W850 (WSR). W10 and WSR have increased in autumn, and the negative trends in SIC typically are collocated with positive trends in W10 and WSR. In winter, W850 has negative trends over the Arctic Ocean, which, together with the lack of decrease of SIC in the central Arctic, has prevented W10 from increasing in winter. The winter trends are notably different from those for autumn, but the correlations are fairly similar. In autumn, winter, and spring, the negative correlation between SIC and W10 originated from the reduction of both stratification and aerodynamic surface roughness z₀ with a reduction of SIC. The dependence of z₀ on SIC is, however, weak in NCEP CFSR. In summer, the ratio of W10 and W850 has increased over large areas. The correlations between SIC and atmospheric variables were stronger on interannual time scales than on subseasonal time scales. The causal relationships are complicated by the twoway interaction between SIC and W10. In most cases, especially in summer, SIC increases after periods of W10 exceeding 5 m s−1.

Large-eddy simulations are widely used to study flows in the atmospheric boundary layer. As atmospheric boundary-layer flows of different atmospheric stratification have very different flow characteristics on different length scales, well-resolved simulations of these flows require very different meshes. The Parallelized Large-Eddy Simulation Model combined with a realizable dynamic subgrid model is used to identify the best method for evaluating the resolution requirements for boundary-layer flows simulated by large-eddy simulations. In particular, we consider three atmospheric boundary-layer set-ups with different stratifications (stable, neutral, convective) to investigate how the quality of the simulation changes with the grid resolution. By following the work of Davidson (Int J Heat Fluid Flow 30(5):1016–1025, 2009), the results are examined using criteria such as the convergence of mean profiles, the ratio of modelled and resolved turbulence kinetic energy, and the two-point correlation. We conclude that the two-point correlation is the best measure to evaluate whether the resolution demands for a specific flow are fulfilled.

Studies have shown that changes in cloud cover are responsible for the rapid climate warming over the Tibetan Plateau (TP) in the past 3 decades. To simulate the total cloud cover, atmospheric models have to reasonably represent the characteristics of vertical overlap between cloud layers. Until now, however, this subject has received little attention due to the limited availability of observations, especially over the TP. Based on the above information, the main aim of this study is to examine the properties of cloud overlaps over the TP region and to build an empirical relationship between cloud overlap properties and large-scale atmospheric dynamics using 4 years (2007–2010) of data from the CloudSat cloud product and collocated ERA-Interim reanalysis data. To do this, the cloud overlap parameter α, which is an inverse exponential function of the cloud layer separation D and decorrelation length scale L, is calculated using CloudSat and is discussed. The parameters α and L are both widely used to characterize the transition from the maximum to random overlap assumption with increasing layer separations. For those non-adjacent layers without clear sky between them (that is, contiguous cloud layers), it is found that the overlap parameter α is sensitive to the unique thermodynamic and dynamic environment over the TP, i.e., the unstable atmospheric stratification and corresponding weak wind shear, which leads to maximum overlap (that is, greater α values). This finding agrees well with the previous studies. Finally, we parameterize the decorrelation length scale L as a function of the wind shear and atmospheric stability based on a multiple linear regression. Compared with previous parameterizations, this new scheme can improve the simulation of total cloud cover over the TP when the separations between cloud layers are greater than 1 km. This study thus suggests that the effects of both wind shear and atmospheric stability on cloud overlap should be taken into account in the parameterization of decorrelation length scale L in order to further improve the calculation of the radiative budget and the prediction of climate change over the TP in the atmospheric models.

The added heat in cities amplifies the health risks of heat waves. At night under calm winds and cloud-free skies, the air in the urban canopy layer can be several degrees warmer than in rural areas. This lower nocturnal cooling in the built-up settings poses severe health risks to the urban inhabitants, as indoor spaces cannot be ventilated effectively. With heat waves becoming more frequent and more intense in future climates, many cities are expanding their green spaces with the aim to introduce cooling through shading, evaporation and lower heat storage capacities. In this study, we assessed how the evening and nighttime cooling effect of urban parks (relative to nearby built-up settings) varies with the park size and the mesoscale atmospheric conditions during warm summer periods. Using a combination of meteorological surface station data and compact radiosondes, the cooling effect is quantified for several urban parks (about 15 ha) and urban woods (about 900 ha). A profiling Doppler wind lidar deployed in the city centre is used to measure turbulent vertical mixing conditions in the urban boundary layer. We find that the maximum nocturnal cooling effects in urban parks range around 1–5 °C during a 1-week heat wave event in mid-July 2022 but also in general during summer 2022 (June–August). Three atmospheric stability and mixing regimes are identified that explain the night-to-night variability in the park cooling effect. We find that very low turbulent vertical mixing in the urban boundary layer (<0.05 m2 s−2) results in the strongest evening cooling in both rural settings and urban parks and the weakest cooling in the built-up environment. This regime specifically occurs during heat waves in connection with large-scale advection of hot air over the region and corresponding subsidence. When nocturnal turbulent vertical mixing above the city is stronger, the evening cooling in urban green spaces is less efficient, so the atmospheric stratification above both urban parks and woods is less stable, and temperature contrasts compared to the built-up environment are less pronounced. These results highlight the fact that urban green spaces have a significant cooling potential during heat waves, with maximum effects at night as advection and mixing transport processes are minimal. This suggests adapting the opening hours of public parks to enable residents to benefit from these cooling islands.