Atmospheric Model Research Articles

Transmission pathways for the stem rust pathogen into Central and East Asia and the role of the alternate host, barberry

Abstract After many decades of effective control of stem rust caused by the Puccinia graminis f.sp. tritici, (hereafter Pgt) the reported emergence of race TTKSK/Ug99 of Pgt in Uganda reignited concerns about epidemics worldwide because ∼90% of world wheat cultivars had no resistance to the new race. Since it was initially detected in Uganda in 1998, Ug99 variants have now been identified in thirteen countries in Africa and the Middle East. Stem rust has been a major problem in the past, and concern is increasing about the risk of return to Central and East Asia. Whilst control programs in North America and Europe relied on the use of resistant cultivars in combination with eradication of barberry (Berberis spp.), the alternate host required for the stem rust pathogen to complete its full lifecycle, the focus in East Asia was principally on the use of resistant wheat cultivars. Here, we investigate potential airborne transmission pathways for stem rust outbreaks in the Middle East to reach East Asia using an integrated modelling framework combining estimates of fungal spore deposition from an atmospheric dispersion model, environmental suitability for spore germination, and crop calendar information. We consider the role of mountain ranges in restricting transmission pathways, and we incorporate a representation of a generic barberry species into the lifecycle. We find viable transmission pathways to East Asia from the Middle East to the north via Central Asia and to the south via South Asia and that an initial infection in the Middle East could persist in East Asia for up to three years due to the presence of the alternate host. Our results indicate the need for further assessment of barberry species distributions in East Asia and appropriate methods for targeted surveillance and mitigation strategies should stem rust incidence increase in the Middle East region.

Examining Tropical Convection Features at Storm‐Resolving Scales Over the Maritime Continent Region

AbstractGlobal Storm Resolving Models (GSRMs) provide a way to understand weather and climate events across scales for better‐informed climate impacts. In this work, we apply the recently developed and validated CAM (Community Atmosphere Model)—MPAS (Model for Prediction Across Scales) modeling framework, based on the open‐source Community Earth System Model (CESM2), to examine the tropical convection features at the storm resolving scale over the Maritime Continent region at 3 km horizontal spacing. We target two global numerical experiments during the winter season of 2018 for comparison with observation in the region. We focus on the investigation of the representations of the convective systems, precipitation statistics, and tropical cyclone behaviors. We found that regional‐refined experiments show more accurate precipitation distributions, diurnal cycles, and better agreement with observations for tropical cyclone features in terms of intensity and strength statistics. We expect the exploration of this work will further advance the development and use of the storm‐resolving model in precipitation predictions across scales.

OH Radical Oxidation of Organosulfates in the Atmospheric Aqueous Phase.

Organosulfates (OS, ROSO3-), ubiquitous constituents of atmospheric particulate matter (PM), influence both the physicochemical and climatic properties of PM. Although the formation pathways of OS have been extensively researched, only a few studies have investigated the atmospheric fate of this class of compounds. Here, to better understand the reactivity and transformation of OS under cloudwater- and aerosol-relevant conditions, we investigate the hydroxyl radical (OH) oxidation bimolecular rate constants (kOS+OHII) and products of five atmospherically relevant OS as a function of pH and ionic strength: methyl sulfate (MeS), ethyl sulfate (EtS), propyl sulfate (PrS), hydroxyacetone sulfate (HaS) and phenyl sulfate (PhS). Our results show that OS are oxidized by OH with kOS+OHII between 108 - 109 M-1 s-1, which corresponds to atmospheric lifetimes of minutes in aqueous aerosol to days in cloudwater. We find that kOS+OHII increases with carbon chain length (MeS < EtS < PrS) and aromaticity (PrS < PhS), but does not depend on solution pH (2, 9). In addition, we find that whereas the OH reactivity of the aliphatic OS studied here decreases by ∼2× with increasing ionic strength (0-15 M), the reactivity of PhS decreases by ∼10×. The oxidation of EtS and PrS produced organic peroxides (ROOH) as first-generation oxidation products, which subsequently photolyzed; the oxidation of PhS resulted in hydroxylated aromatic products. These results highlight the need for inclusion of OS loss pathways in atmospheric models, and suggest caution in using ambient OS concentration measurements alone to estimate their production rates.

Methods to Evaluate Subcolumn Profiles Based on Two‐Point Diagnostics

AbstractIn atmospheric models, stochastic generation of subgrid‐scale profiles or “subcolumns” has been used for a variety of purposes. Such subcolumns can be generated from subgrid probability density functions (PDFs) at different vertical levels, when such PDFs are available. To do so, the generator needs to decide how strongly points should be correlated in the vertical, that is, how much the values should be overlapped. This is sometimes called “PDF overlap.” To assess vertical correlation in a simplified, observable setting, here the vertical correlation of vertical velocity in subcloud layers is examined. Doppler lidar is used to evaluate the vertical profiles of vertical velocity produced by a large‐eddy simulation (LES) model and the Subgrid Importance Latin Hypercube Sampler (SILHS) subcolumn generator. In order to diagnose unrealistic features in subcolumn profiles, various statistical diagnostics are examined here, including the bivariate PDF of vertical velocity at two separated points (i.e., altitudes), the two‐point velocity correlation, the integral correlation length, the PDF of two‐point velocity differences, and the skewness and kurtosis of two‐point velocity differences. The profiles produced by LES match lidar well, except that they are too smooth at small scales. The profiles produced by SILHS exhibit sharp jumps from updraft to downdraft that are not observed in the lidar data. To reduce the generation of these unrealistically sharp jumps, the SILHS sampling method is revised. The diagnostics confirm that the revised sampling method reduces the overprediction of sharp jumps.

Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation.

Прогноз толщины ледяного покрова в прибрежных областях Карского и Охотского морей

Based on the CICE viscoplastic sea ice model, the ice cover of the Gulf of Ob and the northwestern Okhotsk Sea was simulated for the winter seasons of 2021/2022, 2022/2023, and 2023/2024. The description of the seasonal variability of sea ice characteristics in these areas and examples of their numerical modeling are given. Using atmospheric forcing based on the WRF-ARW nonhydrostatic atmospheric model, the sea ice thickness in the northwestern Okhotsk Sea was predicted for April 2024. A comparison of the forecasts with the ice maps of the Hydrometcentre of Russia and sea ice thickness measurements at the Ayan and Bolshoy Shantar stations gave satisfactory results.

Carbonyl Compounds Observed at a Suburban Site during an Unusual Wintertime Ozone Pollution Event in Guangzhou

Carbonyl compounds are important oxygenated volatile organic compounds (VOCs) that play significant roles in the formation of ozone (O3) and atmospheric chemistry. This study presents comprehensive field observations of carbonyl compounds during an unusual wintertime ozone pollution event at a suburban site in Guangzhou, South China, from 19 to 28 December 2020. The aim was to investigate the characteristics and sources of carbonyls, as well as their contributions to O3 formation. Formaldehyde, acetone, and acetaldehyde were the most abundant carbonyls detected, with average concentrations of 7.11 ± 1.80, 5.21 ± 1.13, and 3.00 ± 0.94 ppbv, respectively, on pollution days, significantly higher than those of 2.57 ± 1.12, 2.73 ± 0.88, and 1.10 ± 0.48 ppbv, respectively, on nonpollution days. The Frame for 0-D Atmospheric Modeling (F0AM) box model simulations revealed that local production accounted for 62–88% of observed O3 concentrations during the pollution days. The calculated ozone formation potentials (OFPs) for various precursors (carbonyls and VOCs) indicated that carbonyl compounds contributed 32.87% of the total OFPs on nonpollution days and 36.71% on pollution days, respectively. Formaldehyde, acetaldehyde, and methylglyoxal were identified as the most reactive carbonyls, and formaldehyde ranked top in OFPs, and it alone contributed 15.92% of total OFPs on nonpollution days and 18.10% of total OFPs on pollution days, respectively. The calculation of relative incremental reactivity (RIR) indicates that ozone sensitivity was a VOC-limited regime, and carbonyls showed greater RIRs than other groups of VOCs. The model simulation showed that secondary formation has a significant impact on formaldehyde production, which is primarily controlled by alkenes and biogenic VOCs. The characteristic ratios and backward trajectory analysis also indicated the indispensable impacts of local primary sources (like industrial emissions and vehicle emissions) and regional sources (like biomass burning) through transportation. This study highlights the important roles of carbonyls, particularly formaldehyde, in forming ozone pollution in megacities like the Pearl River Delta region.

Spectral location for the universal scaling regime in Martian atmospheric turbulence

Atmospheric turbulence, irregular fluctuations of the fluid state, is studied on Mars. Universality of the turbulence spectrum underpins atmospheric models where computational requirements preclude full fidelity simulations of the smallest scales. However, there are discrepancies among reports on the existence and spectral location of universal scaling in Martian atmospheric data. Here, results indicate the smallest resolvable structures from Martian wind speed data are still associated with the energetic regime, which may ultimately explain why multiple reports have not found a consistent Kolmogorov-like spectral regime on Mars. Universal spectral scaling of wind data from Perseverance’s Mars Environmental Dynamics Analyzer is used to estimate the thresholds that separate three turbulence regimes: energetic, inertial, and molecular dissipation. Wind measurements at 2-Hz, the fastest sampling rate for direct wind sensor measurements on Mars, resolves turbulence in the energetic regime and approaches the inertial regime, which is consistent with reported Martian dust devil sizes.

Low cloud-SST variability over the summertime subtropical Northeast Pacific: Role of extratropical atmospheric modes

Abstract Over the subtropical Northeast Pacific (NEP), highly reflective low clouds interact with underlying sea-surface temperature (SST) to constitute a local positive feedback. Recent modeling studies showed that, together with wind-evaporation-SST (WES) feedback, the summertime low cloud-SST feedback promotes nonlocal trade wind variations, modulating subsequent evolution of El Niño-Southern Oscillation (ENSO). This study aims to identify drivers of summertime low-cloud variations, using satellite observations and global atmosphere model simulations forced with observed SST. A trans-basin teleconnection is identified, where the North Tropical Atlantic (NTA) warming induced by the North Atlantic Oscillation (NAO) increases precipitation, exciting warm Rossby waves that extend into the NEP. The resultant enhancement of static stability promotes summertime low cloud-SST variability. By regressing out the effects of the preceding ENSO and NTA SST, atmospheric internal variability over the extratropical North Pacific, including the North Pacific Oscillation (NPO), is found to drive the NEP cooling by latent heat loss and subsequent summer low cloud-SST variability. With the help of the background trade winds and WES feedback, the SST anomalies extend southwestward from the low-cloud region, accompanied by ENSO in the following winter. This suggests the nonlocal effects of low clouds identified by recent studies. Analysis of a 500-year climate model simulation corroborates the NTA and NPO forcing of NEP low cloud-SST variability and subsequent ENSO.

Optimised use of interferometry, spectroscopy, and stellar atmosphere models for determining the fundamental parameters of stars

Thanks to recent progress in the field of optical interferometry, instrument sensitivities have now reached the level achieved in the domain of new space missions dedicated to exoplanet and stellar studies. Combining interferometry with other observational approaches enables the determination of stellar parameters and helps improve our understanding of stellar physics. In this paper, we aim to demonstrate a new way of using stellar atmosphere models for a joint interpretation of spectroscopic and interferometric observations. Starting from a discrete grid of one-dimensional (1D) stellar atmosphere models, we developed a training algorithm, based on an artificial neural network, capable of estimating the spectrum and intensity profile of a star over a range of wavelengths and viewing angles. A minimisation algorithm based on the trained function allowed for the simultaneous fitting of the observational spectrum and interferometric complex visibilities. As a result, coherent and precise stellar parameters can be extracted. We show the ability of the trained function to match the modelled intensity profiles of stars in the effective temperature range of $4500$ K to $7000$ K and surface gravity range of $3$ to $5$ dex, with a relative precision to the model that is better than $0.05&lt;!PCT!&gt;$. Using simulated interferometric data and actual spectroscopic measurements, we demonstrated the performance of our algorithm on a sample of five benchmark stars. Using this method, we achieved an accuracy within $0.5&lt;!PCT!&gt;$ for the angular diameter, radius, and surface gravity, and within $20$ K for the effective temperature. This paper demonstrates a new method of using interferometric data combined with spectroscopic observations. This approach offers an improved determination of the radius, effective temperature, and surface gravity of stars.

The Paris low-level jet during PANAME 2022 and its impact on the summertime urban heat island

Abstract. The low-level jet (LLJ) and the urban heat island (UHI) are common nocturnal phenomena. While the UHI has been studied extensively, interactions of the LLJ and the urban atmosphere in general (and the UHI in particular) have received less attention. In the framework of the PANAME (PAris region urbaN Atmospheric observations and models for Multidisciplinary rEsearch) initiative in the Paris region, continuous profiles of horizontal wind speed and vertical velocity were recorded with two Doppler wind lidars (DWLs) – for the first time allowing for a detailed investigation of the summertime LLJ characteristics in the region. Jets are detected for 70 % of the examined nights, often simultaneously at an urban and a suburban site, highlighting the LLJ regional spatial extent. Emerging at around sunset, the mean LLJ duration is ∼ 10 h, the mean wind speed is 9 m s−1, and the average core height is 400 m above the city. The temporal evolution of many events shows signatures that indicate that the inertial oscillation mechanism plays a role in the jet development: a clockwise veering of the wind direction and a rapid acceleration followed by a slower deceleration. The horizontal wind shear below the LLJ core induces variance in the vertical velocity (σw2) above the urban canopy layer. It is shown that σw2 is a powerful predictor for regional contrast in air temperature, as the UHI intensity decreases exponentially with increasing σw2 and strong UHI values only occur when σw2 is very weak. This study demonstrates how DWL observations in cities provide valuable insights into near-surface processes relevant to human and environmental health.

Impacts of Local and Remote SST Warming on Summer Circulation Changes in the Western North Pacific

Abstract This study explores how future SST warming in remote ocean basins may affect the western North Pacific (WNP) wet season climate by applying a high-resolution atmospheric general circulation model to conduct a series of numerical experiments. A marked precipitation and tropical cyclone (TC) activity reduction, as well as enhanced anticyclonic circulation, in the WNP is projected in AMIP experiments forced by SST change in a future warming scenario. The sensitivity experiments reveal that various SST warming phenomena (e.g., in the global SST warming pattern, the tropical ocean belt, the Indian Ocean, the tropical Atlantic, and the subtropical northeast Pacific) and the increase in greenhouse gas concentration could weaken the precipitation, TC activity, and circulation. By contrast, the SST warming in the WNP and eastern equatorial Pacific has opposite and mixed effects, respectively, and tends to weakly offset the dominant influences of remote ocean warming. These results indicate that the WNP, being the epicenter of the global teleconnection of divergent and rotational flow, is susceptible to the influence of the SST warming in remote ocean basins. The remote forcing as projected in future scenarios would overwhelm the enhancing effect of local SST warming and weaken the circulation, convection, and TC activity in the WNP. These findings further the understanding of how the decreased precipitation and enhanced subtropical high in the WNP may be easily triggered by remote SST warming as revealed in the AMIP-type simulations. How this effect would be affected by air–sea coupling needs further investigation.

Molecular Insights into Gas-Particle Partitioning and Viscosity of Atmospheric Brown Carbon.

Biomass burning organic aerosol (BBOA), containing brown carbon chromophores, plays a critical role in atmospheric chemistry and climate forcing. However, the effects of evaporation on BBOA volatility and viscosity under different environmental conditions remain poorly understood. This study focuses on the molecular characterization of laboratory-generated BBOA proxies from wood pyrolysis emissions. The initial mixture, "pyrolysis oil (PO1)", was progressively evaporated to produce more concentrated mixtures (PO1.33, PO2, and PO3) with volume reduction factors of 1.33, 2, and 3, respectively. Chemical speciation and volatility were investigated using temperature-programmed desorption combined with direct analysis in real-time ionization and high-resolution mass spectrometry (TPD-DART-HRMS). This novel approach quantified saturation vapor pressures and enthalpies of individual species, enabling the construction of volatility basis set distributions and the quantification of gas-particle partitioning. Viscosity estimates, validated by poke-flow experiments, showed a significant increase with evaporation, slowing particle-phase diffusion and extending equilibration times. These findings suggest that highly viscous tar ball particles in aged biomass burning emissions form as semivolatile components evaporate. The study highlights the importance of evaporation processes in shaping BBOA properties, underscoring the need to incorporate these factors into atmospheric models for better predictions of BBOA aging and its environmental impact.

Dimethyl sulfide (DMS) climatologies, fluxes, and trends – Part 1: Differences between seawater DMS estimations

Abstract. Dimethyl sulfide (DMS) is a naturally emitted trace gas that can affect the Earth's radiative budget by changing cloud albedo. Most atmospheric models that represent aerosol processes depend on regional or global distributions of seawater DMS concentrations and sea–air flux parameterizations to estimate its emissions. In this study, we analyse the differences between three estimations of seawater DMS, one of which is an observation-based interpolation method following Hulswar et al. (2022) (hereafter referred to as H22) and two of which are proxy-based parameterization methods following Galí et al. (2018) (hereafter referred to as G18) and Wang et al. (2020a) (hereafter referred to as W20). The interpolation-based method depends on the distribution of observations and the methods used to fill data between observations, while the parameterization-based methods rely on establishing a relationship between DMS and environmental parameters such as chlorophyll a, mixed-layer depth, nutrients, sea surface temperature, etc., which can then be used to predict DMS concentrations. On average, the interpolation-based methods show higher DMS values compared to the parameterization-based methods. Even though the interpolation method shows higher values than the parameterization-based methods, it fails to capture mesoscale variability. The regression-based parameterization method (G18) shows the lowest values compared to other estimations, especially in the Southern Ocean, which is the high-DMS region in austral summer. The parameterization-based methods suggest positive long-term trends in seawater DMS (3.82±0.79 % per decade for G18 and 2.13±0.32 % per decade for W20). Since large differences, often more than 100 %, are observed between the different estimations of seawater DMS, the derived sea–air fluxes and, hence, the impact of DMS on the radiative budget are sensitive to the estimate used.

Exploring faint white dwarfs and the luminosity function with Subaru HSC and SDSS in Stripe 82

Abstract We present 5,080 white dwarf (WD) candidates selected from stars matching between the multi-band imaging datasets of the Subaru Hyper Suprime-Cam (HSC) Survey and the Sloan Digital Sky Survey (SDSS) in the Stripe82 region covering about 165 deg2. We select WD candidates from the “reduced proper motion” diagram by combining the apparent magnitude in the range i = 19 – 24 and the proper motion measured from the datasets among a baseline of ∼ 14 years. We refine the WD candidates by fitting blackbody and template WD atmosphere models to HSC photometries for each candidate, enabling the estimation of distance and tangential velocity (vt). The deep HSC data allow us to identify low-temperature (&amp;lt;4000 K) and faint WD candidates down to absolute magnitude, Mbol ≃ 17. We evaluate the selection function of our WD candidates using a mock catalogue of spatial and kinematic distributions of WDs in the (thin and thick) disc and halo regions based on a Galactic model. We construct samples of disc and halo WD candidates by selecting WDs with tangential velocity, 40 &amp;lt; vt/[km s−1] &amp;lt; 80 and 200 &amp;lt; vt/[km s−1] &amp;lt; 500, respectively. The total number densities of the disc and halo WDs are (9.33 ± 0.89) × 10−3 pc−3 and (6.34 ± 2.90) × 10−4 pc−3. Our luminosity functions (LF) extend down to fainter absolute magnitudes compared with previous work. The faint WDs could represent the oldest generation of building blocks over the past ∼10 billion years of the assembly history of our Milky Way.

Assessment of FY-3E GNOS II Radio Occultation Data Using an Improved Three-Cornered Hat Method

The spatial–temporal sampling errors arising from the differences in geographical locations and measurement times between co-located Global Navigation Satellite System (GNSS) radio occultation (RO) and radiosonde (RS) data represent systematic errors in the three-cornered hat (3CH) method. In this study, we propose a novel spatial–temporal sampling correction method to mitigate the sampling errors associated with both RO–RS and RS–model pairs. We analyze the 3CH processing chain with this new correction method in comparison to traditional approaches, utilizing Fengyun-3E (FY-3E) GNSS Occultation Sounder II (GNOS II) RO data, atmospheric models, and RS datasets from the Hailar and Xisha stations. Overall, the results demonstrate that the improved 3CH method performs better in terms of spatial–temporal sampling errors and the variances of atmospheric parameters, including refractivity, temperature, and specific humidity. Subsequently, we assess the error variances of the FY-3E GNOS II RO, RS and model atmospheric parameters in China, in particular the northern China and southern China regions, based on large ensemble datasets using the improved 3CH data processing chain. The results indicate that the FY-3E GNOS II BeiDou navigation satellite system (BDS) RO and Global Positioning System (GPS) RO show good consistency, with the average error variances of refractivity, temperature, and specific humidity being less than 1.12%2, 0.13%2, and 700%2, respectively. A comparison of the datasets from northern and southern China reveals that the error variances for refractivity are smaller in northern China, while temperature and specific humidity exhibit smaller error variances in southern China, which is attributable to the differing climatic conditions.

Impact of Asian Dust on Cirrus Formation Over the Central Pacific: CALIOP‐ and CloudSat‐Observation‐Based Case Studies

AbstractCirrus clouds are of great importance to the global climate, with their net radiative forcing strongly dependent on the microphysical properties that are related to the ice‐nucleating regime. However, the influence of long‐range transport of dust on primary ice formation in cirrus clouds is limitedly understood, specifically over the clean remote ocean regions. Here, two case studies show that transpacific Asian dust can impact the ice formation of cirrus clouds over the central Pacific based on Cloud‐Aerosol Lidar with Orthogonal Polarization and Cloud Profiling Radar (CPR, CloudSat) observations. One case shows a well‐developed horizontally extended cirrus embedded in a pure dust layer, with an average dust‐related ice‐nucleating particle concentration (INPC) of 7 L−1 and 96 L−1 for an ice saturation ratio Si of 1.15 and 1.25, respectively; ice crystal number concentration (ICNC) with diameters &gt;25 and 100 μm (denoted as nice,25 μm and nice,100 μm) are 64 L−1 and 7 L−1, respectively. Another case shows that cirrus clouds with a much smaller horizontal extent appeared in the vicinity of polluted dust, with an average INPC of 42–310 L−1 for the typical higher Si of 1.25–1.35 by considering a tenfold reduction of the ice nucleation efficiency of ice crystals; nice,25 μm and nice,100 μm are 168 L−1 and 20 L−1, respectively. The estimated INPC and ICNC values suggest the dominance of ice formation by dust‐induced heterogeneous nucleation, proving that the long‐range transport of dust toward the upper troposphere and the potential influence on cirrus formation over the central Pacific should be well considered in atmospheric models.

A regularisation technique to precisely infer limb darkening using transit measurements: Can we estimate stellar surface magnetic fields?

Abstract The high-precision measurements of exoplanet transit light curves that are now available contain information about the planet properties, their orbital parameters, and stellar limb darkening (LD). Recent 3D magneto-hydrodynamical (MHD) simulations of stellar atmospheres have shown that LD depends on the photospheric magnetic field, and hence its precise determination can be used to estimate the field strength. Among existing LD laws, the uses of the simplest ones may lead to biased inferences, whereas the uses of complex laws typically lead to a large degeneracy among the LD parameters. We have developed a novel approach in which we use a complex LD model but with second derivative regularisation during the fitting process. Regularisation controls the complexity of the model appropriately and reduces the degeneracy among LD parameters, thus resulting in precise inferences. The tests on simulated data suggest that our inferences are not only precise but also accurate. This technique is used to re-analyse 43 transit light curves measured by the NASA Kepler and TESS missions. Comparisons of our LD inferences with the corresponding literature values show good agreement, while the precisions of our measurements are better by up to a factor of 2. We find that 1D non-magnetic model atmospheres fail to reproduce the observations while 3D MHD simulations are qualitatively consistent. The LD measurements, together with MHD simulations, confirm that Kepler-17, WASP-18, and KELT-24 have relatively high magnetic fields (&amp;gt;200 G). This study paves the way for estimating the stellar surface magnetic field using the LD measurements.

An Empirical Parameterization of the Subgrid‐Scale Distribution of Water Vapor in the UTLS for Atmospheric General Circulation Models

AbstractTemperature and water vapor are known to fluctuate on multiple scales. In this study 27 years of airborne measurements of temperature and relative humidity from In‐service Aircraft for a Global Observing System (IAGOS) are used to parameterize the distribution of water vapor in the upper troposphere and lower stratosphere. The parameterization is designed to simulate water vapor fluctuations within gridboxes of atmospheric general circulation models (AGCMs) with typical size of a few tens to a few hundred kilometers. The distributions currently used in such models are often not supported by observations at high altitude. More sophisticated distributions are key to represent ice supersaturation, a physical phenomenon that plays a major role in the formation of natural cirrus and contrail cirrus. Here the observed distributions are fitted with a beta law whose parameters are adjusted from the gridbox mean variables. More specifically the standard deviation and skewness of the distributions are expressed as empirical functions of the average temperature and specific humidity, two typical prognostic variables of AGCMs. Thus, the distribution of water vapor is fully parameterized for a use in these models. The new parameterization reproduces the observed distributions with a determination coefficient always greater than 0.917 and with a mean value of 0.997. The parameterization is robust to a selection of various geographical subsets of data and to gridbox sizes varying between 25 and 300 km.

Effect of secondary ice production processes on the simulation of ice pellets using the Predicted Particle Properties microphysics scheme

Abstract. Ice pellets can form when supercooled raindrops collide with small ice particles that can be generated through secondary ice production processes. The use of atmospheric models that neglect these collisions can lead to an overestimation of freezing rain. The objective of this study is therefore to understand the impacts of collisional freezing and secondary ice production on simulations of ice pellets and freezing rain. We studied the properties of precipitation simulated with the Predicted Particle Properties (P3) microphysics scheme for two distinct secondary ice production processes. Improvements to the representation of ice pellets and ice crystals in P3 were analyzed by simulating an ice pellet storm that occurred over eastern Canada in January 2020. Those simulations showed that adding secondary ice production processes increased the accumulation of ice pellets but led to unrealistic size distributions of precipitation particles. Realistic size distributions of ice pellets were obtained by modifying the collection of raindrops by small ice particles and the merging criteria of ice categories in P3.