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Evidence of disequilibrium chemistry due to vertical mixing in the atmospheres of many T- and Y-dwarfs has been inferred due to enhanced mixing ratios of CO and reduced NH3. Atmospheric models of planets and brown dwarfs typically parameterize this vertical mixing phenomenon with the vertical eddy diffusion coefficient, K zz . While K zz can perhaps be approximated in the convective regions in the atmosphere with mixing length theory, in radiative regions, the strength of vertical mixing is uncertain by many orders of magnitude. With a new grid of self-consistent 1D model atmospheres from T eff of 400–1000 K, computed with a new radiative-convective equilibrium python code PICASO 3.0, we aim to assess how molecular abundances and corresponding spectra can be used as a probe of depth-dependent K zz . At a given surface gravity, we find nonmonotonic behavior in the CO abundance as a function of T eff, as chemical abundances are sometimes quenched in either of two potential atmospheric convective zones, or quenched in either of two possible radiative zones. The temperature structure and chemical quenching behavior also change with gravity. We compare our models with available near-infrared and M-band spectroscopy of several T- and Y-dwarfs and assess their atmospheric vertical mixing profiles. We also compare to color–magnitude diagrams and make predictions for James Webb Space Telescope spectra. This work yields new constraints, and points the way to significant future gains, in determining K zz , a fundamental atmospheric parameter in substellar atmospheres, with significant implications for chemistry and cloud modeling.

The sensitivity and spectral coverage of JWST are enabling us to test our assumptions of ultracool dwarf atmospheric chemistry, especially with regards to the abundances of phosphine (PH3) and carbon dioxide (CO2). In this paper, we use Near Infrared Spectrograph PRISM spectra (∼0.8−5.5 μm, R ∼ 100) of four late T and Y dwarfs to show that standard substellar atmosphere models have difficulty replicating the 4.1−4.4 μm wavelength range, as they predict an overabundance of phosphine and an underabundance of carbon dioxide. To help quantify this discrepancy, we generate a grid of models using PICASO, based on the Elf Owl chemical and temperature profiles, where we include the abundances of these two molecules as parameters. The fits to these PICASO models show a consistent preference for orders-of-magnitude higher CO2 abundances and a reduction in PH3 abundance as compared to the nominal models. This tendency means that the claimed phosphine detection in UNCOVER−BD−3 could instead be explained by a CO2 abundance in excess of standard atmospheric model predictions; however, the signal-to-noise ratio of the spectrum is not high enough to discriminate between these cases. We discuss atmospheric mechanisms that could explain the observed underabundance of PH3 and overabundance of CO2, including a vertical eddy diffusion coefficient (K zz) that varies with altitude, incorrect chemical pathways, or elements condensing out in forms such as NH4H2PO4. However, our favored explanation for the required CO2 enhancement is that the quench approximation does not accurately predict the CO2 abundance, as CO2 remains in chemical equilibrium with CO after CO quenches.

We reanalyze the near-infrared spectra of the young extrasolar giant planet 51 Eridani b, which was originally presented in Macintosh et al. and Rajan et al. using modern atmospheric models, including a self-consistent treatment of disequilibrium chemistry due to turbulent vertical mixing. In addition, we investigate the possibility that significant opacity from micrometeors or other impactors in the planet’s atmosphere may be responsible for shaping the observed spectral energy distribution (SED). We find that disequilibrium chemistry is useful for describing the mid-infrared colors of the planet’s spectra, especially in regard to photometric data at the M band around 4.5 μm, which is the result of superequilibrium abundances of carbon monoxide, while the micrometeors are unlikely to play a pivotal role in shaping the SED. The best-fitting, micrometeoroid dust–free, disequilibrium chemistry, patchy cloud model has the following parameters: effective temperature T eff = 681 K with clouds (or without clouds, i.e., the grid temperature T grid = 900 K), surface gravity g = 1000 m s−2, sedimentation efficiency f sed = 10, vertical eddy diffusion coefficient K zz = 103 cm2 s−1, cloud hole fraction f hole = 0.2, and planet radius R planet = 1.0 R Jup.

The Venusian atmosphere hosts a 10 km deep convective layer that has been studied by various spacecrafts. However, the impact of the strong vertical mixing on the chemistry of this region is still unknown. This study presents the first realistic coupling between resolved small‐scale turbulence and a chemical network. The resulting vertical mixing is different for each species: those with longer chemical timescales will tend to be well‐mixed. Vertical eddy diffusion due to resolved convection motions was estimated, ranging from 102 to 104 m2/s for the 48–55 km convective layer, several orders of magnitude above the typically used value. In the 48–55 km convective layer, the impact of the small‐scale turbulence on the cloud layer boundaries was between 200 m and 1 km. The impact of turbulence on cloud chemistry is consistent with Venus Express/Visible and Infrared Thermal Imaging Spectrometer observations. The observability at the cloud‐top of small‐scale turbulence by VenSpec‐U spectrometer would be challenging. Plain Language Summary Venus hosts a global sulfuric acid cloud layer between 45 and 70 km. A convective layer is present between roughly 50 and 60 km, with its variability in latitude and local time assessed by observation, with a thicker layer at high latitude and at night. One question that remains unclear is how this turbulence mixes momentum, heat, and chemical species. Especially, the impact of the strong vertical mixing on the chemistry of this region is still unknown. To investigate this topic, we use a convection‐resolving model coupled for the first time with a realistic chemical network. The resulting vertical mixing is different for each species: those with longer chemical timescales will tend to be well‐mixed. 1D and global circulation models use the so‐called vertical eddy diffusion approach to represent turbulent motion, quantified in our model and underestimated in chemistry models. The small‐scale turbulence in the cloud layer causes a variation in the altitude of the top and bottom boundaries of the cloud. Our model shows that the impact of turbulence on cloud chemistry corresponds well to what has been observed by satellites. In the future, the EnVision mission will be able to observe chemical species at the small turbulence scales. Key Points Estimation for the first time of the spatial and temporal variability of chemical species due to vertical mixing Quantification of the vertical eddy diffusion coefficient, order of magnitude above typical used values Cloud‐top altitudes change by 0.2–1 km due to vertical convective mixing and gravity waves

It is well known that the urban canopy (UC) layer, i.e., the layer of air corresponding to the assemblage of the buildings, roads, park, trees and other objects typical to cities, is characterized by specific meteorological conditions at city scales generally differing from those over rural surroundings. We refer to the forcing that acts on the meteorological variables over urbanized areas as the urban canopy meteorological forcing (UCMF). UCMF has multiple aspects, while one of the most studied is the generation of the urban heat island (UHI) as an excess of heat due to increased absorption and trapping of radiation in street canyons. However, enhanced drag plays important role too, reducing mean wind speeds and increasing vertical eddy mixing of pollutants. As air quality is strongly tied to meteorological conditions, the UCMF leads to modifications of air chemistry and transport of pollutants. Although it has been recognized in the last decade that the enhanced vertical mixing has a dominant role in the impact of the UCMF on air quality, very little is known about the uncertainty of vertical eddy diffusion arising from different representation in numerical models and how this uncertainty propagates to the final species concentrations as well as to the changes due to the UCMF.To bridge this knowledge gap, we set up the Regional Climate Model version 4 (RegCM4) coupled to the Comprehensive Air Quality Model with Extensions (CAMx) chemistry transport model over central Europe and designed a series of simulations to study how UC affects the vertical turbulent transport of selected pollutants through modifications of the vertical eddy diffusion coefficient (Kv) using six different methods for Kv calculation. The mean concentrations of ozone and PM2.5 in selected city canopies are analyzed. These are secondary pollutants or having secondary components, upon which turbulence acts in a much more complicated way than in the case of primary pollutants by influencing their concentrations not only directly but indirectly via precursors too. Calculations are performed over cascading domains (of 27, 9, and 3 km horizontal resolutions), which further enables to analyze the sensitivity of the numerical model to grid resolution. A number of model simulations are carried out where either urban canopies are considered or replaced by rural ones in order to isolate the UC meteorological forcing. Apart from the well-pronounced and expected impact on temperature (increases up to 2 ∘C) and wind (decreases by up to 2 ms-1), there is a strong impact on vertical eddy diffusion in all of the six Kv methods. The Kv enhancement ranges from less than 1 up to 30 m2s-1 at the surface and from 1 to 100 m2s-1 at higher levels depending on the methods. The largest impact is obtained for the turbulent kinetic energy (TKE)-based methods.The range of impact on the vertical eddy diffusion coefficient propagates to a range of ozone (O3) increase of 0.4 to 4 ppbv in both summer and winter (5 %–10 % relative change). In the case of PM2.5, we obtained decreases of up to 1 µgm-3 in summer and up to 2 µgm-3 in winter (up to 30 %–40 % relative change). Comparing these results to the “total-impact”, i.e., to the impact of all meteorological modifications due to UCMF, we can conclude that much of UCMF is explained by the enhanced vertical eddy diffusion, which counterbalances the opposing effects of other components of this forcing (temperature, humidity and wind). The results further show that this conclusion holds regardless of the resolution chosen and in both the warm and cold parts of the year.

Accurately characterizing aerosol vertical distributions is essential for evaluating radiative forcing and air quality. While Chemical Transport Models (CTMs) simulate spatially continuous Aerosol Extinction Coefficient (AEC, km.sup.-1 ), they exhibit systematic AEC biases. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations provide precise AEC profiles but are constrained by sparse spatial sampling. To bridge this gap, we propose a physics-informed Transformer framework as a supervised bias-correction model to correct biases in the AEC profiles simulated by GEOS-Chem. Unlike a standard Transformer, our framework features a dual-stream architecture with explicit physical constraints. It employs gated feature fusion to integrate vertical structures (combining GEOS-Chem priors with MERRA-2 profiles) by dynamically identifying height-dependent drivers, and leverages cross-attention to incorporate MERRA-2 surface environmental constraints for modulating AEC vertical rectification with synoptic contexts. This approach effectively predicts systematic biases relative to CALIOP satellite observations and resolves AEC profiles, surpassing methods retrieving only aerosol layer heights. Leave-One-Year-Out validation over East Asia during 2017-2019 demonstrates significant AEC precision improvements, increasing R from 0.49-0.53 in the GEOS-Chem simulations to 0.66-0.73 and reducing RMSE by approximately 25 %. The model effectively mitigates over-diffusion, significantly reducing AEC simulation biases in the critical near-surface layer while capturing smoothed biomass burning and dust plumes. Additionally, it exhibits robust cross-continental transferability, reproducing bias patterns over the North American domain (R=0.70) without retraining, confirming the internalization of universal physicochemical relationships linking atmospheric states to simulation biases. Furthermore, interpretability analysis serves as a diagnostic tool to guide physical model improvement. The model identifies temperature and sensible heat flux as primary drivers to constrain boundary layer mixing, pointing to potential uncertainties in vertical eddy diffusion. Additionally, it uses environmental proxies (e.g., vegetation indices and diffuse radiation) to diagnose potential deficiencies in dust threshold friction velocity and secondary organic aerosol yields. These insights provide a physical basis for refining parameterization schemes in CTMs.

Rural-to-urban transformation (RUT) is the process of turning a rural or natural land surface into an urban one, which brings about important modifications in the surface, causing well-known effects like the urban heat island (UHI), reduced wind speeds, and increased boundary layer heights. Moreover, with concentrated human activities, RUT introduces new emission sources which greatly perturb local and regional air pollution. Particulate matter (PM) is one of the key pollutants responsible for the deterioration of urban air quality and is still a major issue in European cities, with frequent exceedances of limit values. Here we introduce a regional chemistry–climate model (regional climate model RegCM coupled offline to chemistry transport model CAMx) study which quantifies how the process of RUT modified the PM concentrations over central Europe including the underlying controlling mechanisms that contribute to the final PM pollution. Apart from the two most studied ones, (i) urban emissions and (ii) urban canopy meteorological forcing (UCMF; i.e. the impact of modified meteorological conditions on air quality), we also analyse two less studied contributors to RUT's impact on air quality: (iii) the impact of modified dry-deposition velocities (DVs) due to urbanized land use and (iv) the impact of modified biogenic emissions due to urbanization-induced vegetation modifications and changes in meteorological conditions which affect these emissions. To calculate the magnitude of each of these RUT contributors, we perform a cascade of simulations, whereby each contributor is added one by one to the reference state, while focus is given on PM2.5 (particulate matter with diameter less then 2.5 µm). Its primary and secondary components, namely primary elemental carbon (PEC), sulfates (PSO4), nitrates (PNO3), ammonium (PNH4), and secondary organic aerosol (SOA), are analysed too. The validation using surface measurements showed a systematic negative bias for the total PM2.5, which is probably caused by underestimated organic aerosol and partly by the negative bias in sulfates and elemental carbon. For ammonium and nitrates, the underestimation is limited to the warm season, while for winter, the model tends to overestimate their concentrations. However, in each case, the annual cycle is reasonably captured. We evaluated the RUT impact on PM2.5 over a sample of 19 central European cities and found that the total impact of urbanization is about 2–3 and 1–1.5 µg m−3 in winter and summer, respectively. This is mainly driven by the impact of emissions alone causing a slightly higher impact (1.5–3.5 and 1.2–2 µg m−3 in winter and summer), while the effect of UCMF was a decrease at about 0.2–0.5 µg m−3 (in both seasons), which was mainly controlled by enhanced vertical eddy diffusion, while increases were modelled over rural areas. The transformation of rural land use into an urban one caused an increase in dry-deposition velocities by around 30 %–50 %, which alone resulted in a decrease in PM2.5 by 0.1–0.25 µg m−3 in both seasons. Finally, the impact of biogenic emission modifications due to modified land use and meteorological conditions caused a decrease in summer PM2.5 of about 0.1 µg m−3, while the winter effects were negligible. The total impact of urbanization on aerosol components is modelled to be (values indicate winter and summer averages) 0.4 and 0.3 µg m−3 for PEC, 0.05 and 0.02 µg m−3 for PSO4, 0.1 and 0.08 µg m−3 for PNO3, 0.04 and 0.03 µg m−3 for PNH4, and 0 and 0.05 µg m−3 for SOA. The main contributor of each of these components was the impact of emissions, which was usually larger than the total impact due to the fact that UCMF was counteracted with a decrease. For each aerosol component, the impact of modified DV was a clear decrease in concentration, and finally, the modifications of biogenic emissions impacted SOA predominantly, causing a summer decrease, while a very small secondary effect of secondary inorganic aerosol was modelled too (they increased). In summary, we showed that when analysing the impact of urbanization on PM pollution, apart from the impact of emissions and the urban canopy meteorological forcing, one also has to consider the effect of modified land use and its impact on dry deposition. These were shown to be important in both seasons. For the effect of modified biogenic emissions, our calculations showed that they act on PM2.5 predominantly through SOA modifications, which only turned out to be important during summer.

Urban areas are hot spots of intense emissions, and they influence air quality not only locally but on a regional or even global scale. The impact of urban emissions over different scales depends on the dilution and chemical transformation of the urban plumes which are governed by the local- and regional-scale meteorological conditions. These are influenced by the presence of urbanized land surface via the so-called urban canopy meteorological forcing (UCMF). In this study, we investigate for selected central European cities (Berlin, Budapest, Munich, Prague, Vienna and Warsaw) how the urban emission impact (UEI) is modulated by the UCMF for present-day climate conditions (2015–2016) using two regional climate models, the regional climate models RegCM and Weather Research and Forecasting model coupled with Chemistry (WRF-Chem; its meteorological part), and two chemistry transport models, Comprehensive Air Quality Model with Extensions (CAMx) coupled to either RegCM and WRF and the “chemical” component of WRF-Chem. The UCMF was calculated by replacing the urbanized surface by a rural one, while the UEI was estimated by removing all anthropogenic emissions from the selected cities. We analyzed the urban-emission-induced changes in near-surface concentrations of NO2, O3 and PM2.5. We found increases in NO2 and PM2.5 concentrations over cities by 4–6 ppbv and 4–6 µg m−3, respectively, meaning that about 40 %–60 % and 20 %–40 % of urban concentrations of NO2 and PM2.5 are caused by local emissions, and the rest is the result of emissions from the surrounding rural areas. We showed that if UCMF is included, the UEI of these pollutants is about 40 %–60 % smaller, or in other words, the urban emission impact is overestimated if urban canopy effects are not taken into account. In case of ozone, models due to UEI usually predict decreases of around −2 to −4 ppbv (about 10 %–20 %), which is again smaller if UCMF is considered (by about 60 %). We further showed that the impact on extreme (95th percentile) air pollution is much stronger, and the modulation of UEI is also larger for such situations. Finally, we evaluated the contribution of the urbanization-induced modifications of vertical eddy diffusion to the modulation of UEI and found that it alone is able to explain the modeled decrease in the urban emission impact if the effects of UCMF are considered. In summary, our results showed that the meteorological changes resulting from urbanization have to be included in regional model studies if they intend to quantify the regional footprint of urban emissions. Ignoring these meteorological changes can lead to the strong overestimation of UEI.

Based on the quality-controlled observational spectral width data of the Beijing mesosphere–stratosphere–troposphere (MST) radar in the altitudinal range of 3–19.8 km from 2012 to 2014, this paper analyses the relationship between the proportion of negative turbulent kinetic energy (N-TKE) and the horizontal wind speed and the vertical shear of horizontal wind domain and gives the distributional characteristics of atmospheric turbulence parameters obtained by using different calculation models. Three calculation models of the spectral width method were used in this study – namely the H model (Hocking, 1985), N-2D model (Nastrom, 1997) and D–H model (Dehghan and Hocking, 2011). The results showed that the proportion of N-TKE in the H model, N-2D model and D–H model increases with the horizontal wind speed u and/or the vertical shear of horizontal wind speed ∂u∂z, and the maximum values are 60 %, 45 % and 35 %, respectively. When the∂u∂z is greater than 0.006 s−1, the N-TKE of the H model increases sharply with ∂u∂z; the increase rate is about 20%0.002s-1. For these three models, the results are similar except that the vertical shear of the horizontal wind speed is greater than 0.006 s−1. When ∂u∂z>0.006 s−1, the proportion of N-TKE in the N-2D and H models increases with ∂u∂z, while the proportion in the D–H model is less than 10 % and has slight variation. However, it is still necessary to consider the applicability of the N-2D model and D–H model in some weather processes with strong winds. The distributional characteristics with height of the turbulent kinetic energy dissipation rate ε and the vertical eddy diffusion coefficient Kz derived by the three models are consistent with previous studies. Still, there are differences in the values of turbulence parameters. Also, the range resolution of the radar has little effect on the differences in the range of turbulence parameters' values. The median values of ε in the H model, N-2D model and D–H model is 10−3.2–10−2.7, 10−3.0–10−2.6 and 10−3.3–10−2.8 m2 s−3, respectively. The median values of Kz in these three models are 100.3–100.7, 100.4–100.7 and 100.1–100.5 m2 s−1.

The vertical eddy diffusion process plays a crucial role in PM2.5 prediction, yet accurately predicting it remains challenging. In the three-dimensional atmospheric chemistry transport model (3-D AQM) CMAQ, a parameter, Kz, is utilized, and it is known that PM2.5 prediction tendencies vary according to the floor value of this parameter (Kzmin). This study aims to examine prediction characteristics according to Kzmin values, targeting days exceeding the Korean air quality standards, and to derive appropriate Kzmin values for predicting PM2.5 concentrations in the DJFM Seoul Metropolitan Area (SMA). Kzmin values of 0.01, 0.5, 1.0, and 2.0, based on the model version and land cover, were applied as single values. Initially focusing on December 4th to 12th, 2020, the prediction characteristics were examined during periods of local and inflow influence. Results showed that in both periods, as Kzmin increased, surface concentrations over land decreased while those in the upper atmosphere increased, whereas over the sea, concentrations increased in both layers due to the influence of advection and diffusion without emissions. During the inflow period, the increase in vertically diffused pollutants led to increased inflow concentrations and affected contribution assessments. Long-term evaluations from December 2020 to March 2021 indicated that the prediction performance was superior when Kzmin was set to 0.01, but it was not significant for the upwind region (China). To improve trans-boundary effects, optimal values were applied differentially by region (0.01 for Korea, 1.0 for China, and 0.01 for other regions), resulting in significantly improved prediction performance with an R of 0.78, IOA of 0.88, and NMB of 0.7%. These findings highlight the significant influence of Kzmin values on winter season PM2.5 prediction tendencies in the SMA and underscore the need for considering differential application of optimal values by region when interpreting research and making policy decisions.