Atmospheric aerosols play an important role in the Earth's climate system. We present the analysis of atmospheric molecules/particles collected with a sampling system that can fly under regular weather balloons. The flights took place on 10 October 2022 from Reims and on 13 December 2022 from Orléans (France). The samples collected on activated carbon filters were analyzed by high-resolution mass spectrometry (Orbitrap Q-Exactive). Using Desorption electrospray ionization (DESI), we could derive hundreds of chemical formulas for organic species present in different layers from the troposphere to the stratosphere (up to 20 km). Measurements of O3, CO, and aerosol concentrations a few hours before these flights took place to contextualize the sampling.

Stratiform and convective precipitation are known to be associated with distinct isotopic fingerprints in the tropics. Such rain type specific isotope signals are of key importance for climate proxies based on stable isotopes like for example ice cores and tree rings and can be used for climate reconstructions of convective activity. However, recently, the relation between rain type and isotope signal has been intensively discussed. While some studies point out the importance of deep convection for strongly depleted isotope signals in precipitation, other studies emphasize the role of stratiform precipitation for low concentrations of the heavy water isotopes. Uncertainties arise from observational studies as they mainly consider oceanic regions and mostly long aggregation timescales, while modelling approaches with global climate models cannot explicitly resolve convective processes and rely on parametrization. As high-resolution climate models are particularly important for studies over complex topography, we applied the isotope-enabled version of the high-resolution climate model from the Consortium for Small-Scale Modelling (COSMOiso) over the Andes of tropical south Ecuador, South America, to investigate the influence of stratiform and convective rain on the stable oxygen isotope signal of precipitation (δ18OP). Our results highlight the importance of deep convection for depleting the isotopic signal of precipitation and increasing the secondary isotope variable deuterium excess. Moreover, we found that an opposing effect of shallow and deep convection on the δ18OP signal. Based on these results, we introduce a shallow and deep convective fraction to analyze the effect of rain types on δ18OP.

The Regional Climate Modeling system (RegCM) has undergone a significant evolution over the years, leading for example to the widely used versions RegCM4 and RegCM4-NH. In response to the demand for higher resolution, a new version of the system has been developed, RegCM5, incorporating the non-hydrostatic dynamical core of the MOLOCH weather prediction model. In this paper we assess the RegCM5’s performance for 5 CORDEX-CORE domains, including a pan-European domain at convection-permitting resolution. We find temperature biases generally in the range of -2 to 2 degrees Celsius, higher in the northernmost regions of North America and Asia during winter, linked to cloud water overestimation. Central Asia and the Tibetan Plateau show cold biases, possibly due to sparse station coverage. The model exhibits a prevailing cold bias in maximum temperature and warm bias in minimum temperature, associated with a systematic overestimation of lower-level cloud fraction, especially in winter. Taylor diagrams indicate a high spatial temperature pattern correlation with ERA5 and CRU data, except in South America and the Caribbean region. The precipitation evaluation shows an overestimation in South America, East Asia, and Africa. RegCM5 improves the daily precipitation distribution compared to RegCM4, particularly at high intensities. The analysis of wind fields confirms the model’s ability to simulate monsoon circulations. The assessment of tropical cyclone tracks highlights a strong sensitivity to the tracking algorithms, thus necessitating a careful model interpretation. Over the European region, the convection permitting simulations especially improve the diurnal cycle of precipitation and the hourly precipitation intensities.

The 2022 Hunga eruption led to extraordinary water vapor enhancement throughout the stratospheric vortex at the beginning of the 2023 Antarctic winter. Although the dynamical characteristics of the vortex itself were generally unexceptional, the excess moisture initially raised the threshold temperatures for the formation of polar stratospheric clouds (PSCs) above typical values over a broad vertical domain. Low temperatures, especially during an early-July cold spell, prompted ice PSC formation and unusually severe irreversible dehydration at higher levels (500–700 K), while atypical rehydration occurred at lower levels (380–460 K). Heterogeneous chemical processing was more extensive, both vertically (up to 750–800 K) and temporally (earlier in the season), than in prior Antarctic winters. The resultant HCl depletion and ClO enhancement both redefined their previously observed ranges at and above 600 K. Albeit unmatched in the satellite record, the early-winter upper-level chlorine activation was insufficient to induce substantial ozone loss. Chlorine activation, denitrification, and dehydration processes saturated in midwinter, with trace gas evolution essentially following the climatological mean thereafter. Chlorine deactivation started slightly later than in most years. While cumulative ozone losses at 410–550 K were relatively large, probably because of the delayed chlorine deactivation, they were not unprecedented. Thus, ozone depletion was unremarkable throughout the lower stratosphere. Although Hunga hastened the onset of and increased the vertical extent of PSC formation and chlorine activation in early winter, saturation of lower stratospheric chemical processing (as is typical in the Antarctic) prevented an exceptionally severe ozone hole in 2023.

Many atmospheric river detectors (ARDTs) have been developed over the past few decades to capture atmospheric rivers (ARs). However, different ARDTs have been observed to capture different frequencies, shapes and sizes of ARs. Due to this, many questions including investigating the underlying phenomena for ARs in the ARDTs have been posed. In this paper, we assess four different ARDTs and investigate the underlying meteorological phenomena during landfalling ARs. We find that during landfalling ARs events, there exists a prevalent low-pressure and high-pressure confluence that enhances moisture influx toward the landfalling site. The strength of the pressure gradient in the confluence region enhances the influx of the integrated vapor transport. The four ARDTs predominantly capture similar atmospheric processes, nonetheless, they have statistically different magnitudes.

Unsteady land-sea breezes (LSBs) resulting from time-varying surface thermal contrasts Δθ(t) are explored in the presence of a constant synoptic pressure forcing, Mg, when the latter is oriented from sea to land (α=0°), versus land to sea (α=180°). Large eddy simulations reveal the development of four distinctive regimes depending on the joint interaction between (Mg, α) and Δθ(t) in modulating the fine-scale dynamics. Time lags, computed as the shifts that maximize correlation coefficients of the dynamics between transient and the corresponding steady state scenarios at Δθ=Δθmax, are found to be significant and to extend 2 hours longer for α=0° compared to α=180°. These diurnal dynamics result in non-equilibrium flows that behave differently over the two patches for both α’s. Turbulence is found to be out of equilibrium with the mean flow, and the mean itself is found to be out of equilibrium with the thermal forcing. The sea surface heat flux is consistently more sensitive than its land counterpart to the time-varying external forcing Δθ(t), and more so for synoptic forcing from land-to-sea (α=180°). Hence, although the land reaches equilibrium faster, the sea patch is found to exert a stronger control on the final turbulence-mean flow equilibrium response. Finally, vertical velocity profile at the shore and shore-normal velocity transects at the first grid level are shown to encode the multiscale regimes of the LSBs evolution, and can thus be used to identify these regimes using k-means clustering.

A document by Megan Purchase. Click on the document to view its contents.

Understanding the vertical structure of atmospheric aerosol is important for solar radiometric study across all spectra. This work pertains to three relevant Southern Hemispheric regions of interest: Southeast Atlantic (SEA), Amazonia (AMZ), and Southeast Pacific (SEP), where seasonal biomass burning events produce smoke plumes of climatic interest. We make use of our previously validated aerosol typology based in AERONET retrieved optical properties, to identify each individual measurement classified as biomass burning within the geographic region of interest. The data is trimmed to select only those classifications measured within the recognized fire-dominant season of each geographic region. We employ The European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis of Copernicus Atmosphere Monitoring Service (CAMS) Carbon Monoxide (CO) data to construct canonical vertical CO profiles (characteristic "shape" curves) from records in the historic burn season window, and within the geographic rectangular boundaries of interest. These canonical CO curves then proxy for AOD curves, constrained to be distributed vertically such that their integrated sum matches to specific Bulk Columnar AOD (BCA) values as determined in the corresponding AERONET record. The layer CO values are normalized to fractional coefficients of the columnar CO total for each canonical profile. These coefficients then are distributed as AOD layer coefficients of the bulk columnar AOD; thus, preserving the canonical profile shapes. This results in vertically resolved AOD profiles for specific geo-region which can be fed into a Radiative Transfer model to result in Total Layered Heating Rates (TLHR) and Aerosol Layered Heating Rates (ALHR) expressed in K/day. We found for example: smoke aerosol plumes in the SEA during the August to October season tend to bi-modally develop between a characteristically higher plume or a lower plume, separated by approximately 1 km vertically. Layer Heating rates develop accordingly. We present methodology, developments, and some cases of these studies specifically for the Southeast Atlantic (SEA) region dominated by seasonal wildfire in Sub-Sahel Africa.

Satellite-observed microwave radiances provide information on both surface and atmosphere. For operational weather forecasting, information on atmospheric temperature, humidity, cloud and precipitation is directly inferred using all-sky radiance data assimilation. In contrast, information on the surface state, such as sea surface temperature (SST) and sea ice fraction, is typically provided through third-party retrieval products. Scientifically, this is a sub-optimal use of the observations, and practically it has disadvantages such as time delays of more than 48 hours. A better solution is to jointly estimate the surface and atmospheric state from the radiance observations. This has not been possible until now due to incomplete knowledge of the surface state and the radiative transfer that links this to the observed radiances. A new approach based on an empirical state and empirical sea ice surface emissivity model is used here to add sea ice state estimation, including sea ice concentration (SIC), to the European Centre for Medium-range Weather Forecasts atmospheric data assimilation system. The sea ice state is estimated using augmented control variables at the observation locations. The resulting SIC estimates are of good quality and they highlight apparent defects in the existing OCEAN5 sea ice analysis. The SIC estimates can also be used to track giant icebergs, which may provide a novel maritime application for passive microwave radiances. Further, the SIC estimates should be suitable for onward use in coupled ocean-atmosphere data assimilation. There is also increased coverage of microwave observations in the proximity of sea ice, leading to improved atmospheric forecasts out to day 4 in the Southern Ocean.

It has been found that wintertime mixed-phase cloud properties can present significant differences based on the degree of interaction with air masses coming from locations with reduced sea ice concentration or high presence of sea ice leads. When these air masses are represented by the water vapor transport (WVT) which can interact with the clouds, the properties of the clouds show contrasting differences with respect to cases where the WVT is not interacting with the cloud, i.e. it is not coupled to the cloud. These findings have been reported first for the analysis of the MOSAiC expedition dataset from 2019 to 2020 in the central Arctic \cite{Shupe_2022,Saavedra_Garfias_2023}. In the present contribution, we expand that analysis to long-term measurements (2012-2022) at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) at the North Slope Alaska (NSA) site in Utqiaǵvik, Alaska. Based on those 10 years of characterized cloud and  sea ice properties, statistically more robust analysis is performed to support or contradict the MOSAiC results. Furthermore, the statistically richer data set from NSA allows to narrow down cases where the properties or coupled clouds to WVT are substantially dissimilar to decoupled cases. Among those are the increase of liquid water path correlated to a decrease of sea ice concentration and ice water paths which are not exhibiting an influence by sea ice concentration. The thermodynamic phase of the clouds also exposes differences based on the state of coupling among the cloud--WVT--sea ice system. These results are put into consideration for the modeling community since sea ice leads are not explicitly resolved in such models, thus the sea ice leads or polynyas effects to processes responsible for mixed-phase cloud formation/dissipation and thermodynamic phase balance are of considerable interest for the parametrization of energy exchange between the surface and the atmosphere in the Arctic.AGU 2023 Session Selection: A093. Microphysical and Macrophysical Properties and Processes of Ice and Mixed-Phase Clouds: Linking in Situ and Remote Sensing Observations and Multiscale Models.

Based on wintertime observations during the MOSAiC expedition in 2019-2020 \cite{Shupe_2022}, it has been found that Arctic cloud properties show significant differences when clouds are coupled to the fluxes of water vapor transport (WVT) coming from upwind regions of sea ice leads \cite{Saavedra_Garfias_2023,saavedragarfias2023}. Mixed-phase clouds (MPC) were characterized by the Cloudnet algorithm using observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) mobile facility and the Leibniz Institute for Tropospheric Research (TROPOS) OCEANet facility, both on board the RV Polarstern . A coupling mechanism to entangle the upwind sea ice leads via the water vapor transport entraintment to the cloud layer has been proposed to successfully identify differences of MPC properties under and without the influence of WVT. For MPC below 3 km liquid water path was found to be increasingly influenced by sea ice lead fraction whereas ice water path was not significantly different in the presence of sea ice leads. However, the ice water fraction, defined as the fraction of ice water path to the total water path, was exhibiting distinguishable asymmetries for cases of MPC coupled to WVT versus decoupled cases. Mainly, the ice water fractions of MPC coupled to WVT were monotonically increasing with decreasing cloud top temperature, while the decoupled cases show increases and decreases in ice water fraction at some specific temperature ranges. The dissimilar behavior of ice water fraction suggests that WVT could importantly influence the processes responsible for heterogeneous ice formation and solid precipitation, therefore coupled MPC and the ice water fraction was also analyzed as a function of snowfall rates at ground. These characteristics are presented based on case studies where WVT back trajectories are available to have a deeper understanding of the interaction processes with sea ice leads that drives the cloud coupled/decoupled differences. Moreover the statistics of our findings based on the whole MOSAiC wintertime period will be put into  consideration.\cite{von_Albedyll_2023}\cite{Shupe_2022}\cite{saavedragarfias2022}AGU 2023 Session Selection: C014. Coupled-system Processes of the Central Arctic Atmosphere-Sea Ice-Ocean System: Harnessing Field Observations and Advancing Models.

A document by Suqin Duan. Click on the document to view its contents.

Super-coarse dust particles (diameters > 10 µm) are evidenced to be more abundant in the atmosphere than model estimates and contribute significantly to the dust climate impacts.  Since super-coarse dust accounts for less dust extinction in the visible-to-near-infrared (VIS-NIR) than in the thermal infrared (TIR) spectral regime, they are suspected to be underestimated by remote sensing instruments operates only in VIS-NIR, including Aerosol Robotic Networks (AERONET), a widely used dataset for dust model validation. In this study, we perform a radiative closure assessment using the AERONET-retrieved size distribution in comparison with the collocated Atmospheric Infrared Sounder (AIRS) TIR observations with comprehensive uncertainty analysis. The consistently warm bias in the comparisons suggests a potential underestimation of super-coarse dust in the AERONET retrievals due to the limited VIS-NIR sensitivity. An extra super-coarse mode included in the AERONET-retrieved size distribution helps improve the TIR closure without deteriorating the retrieval accuracy in the VIS-NIR.

Urban Heat Island (UHI) effects have significant implications on the microclimatic conditions in urban environments, impacting human health, energy consumption, and overall urban planning. This study aims to assess the diurnal intensity of UHI in a microclimatic urban setting by adopting the Local Climate Zone (LCZ) classification approach. We utilized a combination of remote sensing data, ground-based measurements, and LCZ classification to analyze the temporal and spatial variation of UHI intensity throughout the day and night. The study area, Dehradun city, a densely populated urban area situated in the valley region of Himalayas, exhibited diverse LCZs, including compact low-rise, dense trees, and open spaces. Using satellite-derived land surface temperature (LST) data and hourly in-situ measurements, we quantified the UHI effect during daytime and nighttime hours. The results revealed distinct diurnal patterns of UHI intensity among different LCZs, with peak intensity occurring during late afternoon and early evening hours. Furthermore, we investigated the impact of vegetation and built-up characteristics on UHI variation, highlighting the cooling effect of green spaces and the amplifying effect of impervious surfaces. This research contributes to a better understanding of microclimatic urban environments and their relation to UHI dynamics, providing valuable insights for urban planners, policymakers, and researchers aiming to mitigate heat-related issues and promote sustainable urban development. The findings underscore the importance of considering local land-use patterns and urban morphology when assessing and managing UHI effects.

A document by Sandy Hardian Susanto Herho. Click on the document to view its contents.

The accurate estimation of spectral power-law exponents (β) in atmospheric gravity wave (GW) spectra plays a crucial role in understanding energy transfer mechanisms within the atmosphere. However, dealing with observational gaps in atmospheric measurements poses a significant challenge for obtaining reliable estimations. Using simulated data of varying complexity, we rigorously evaluate the performance of these estimation methods and aim to offer valuable guidance in selecting the most appropriate approach for accurately determining β from observational datasets with gaps. Our findings are of paramount importance for researchers, as they can significantly impact our understanding of energy propagation and dissipation in the atmosphere.To further extend the scope and applicability of our investigation, we have embarked on analyzing lidar measurements of temperatures and winds in the stratosphere and mesosphere above Kühlungsborn (54°N, 12°E) and ALOMAR (69°N, 16°E). By estimating the spectral power-law exponents of atmospheric GWs in these distinct regions, we intend to shed light on the energy transfer processes occurring during complex atmospheric dynamics in the vicinity of the polar vortex.This research aims to bridge the gap between theoretical predictions and empirical findings in atmospheric science by providing robust methodologies for accurate spectral estimation, even in the presence of sparse observational data.

The balance of processes affecting electron density drives the dynamics of upper-atmospheric electrical events, such as sprites. We examine the detachment of electrons from negatively charged atomic oxygen (O-) via collisions with neutral molecular nitrogen (N2) leading to the formation of nitrous oxide (N2O). Past research posited that this process, even without significant vibrational excitation of N2, strongly impacts the dynamics of sprites. We introduce updated rate coefficients derived from recent experimental measurements which suggest a negligible influence of this reaction on sprite dynamics. Given that previous rates were incompatible with the observed persistence times of luminous features in sprites, our findings support that these features result from electron depletion in sprite columns.

Utilizing atmospheric temperature observed from Mars Years 33 to 36 by the Imaging Ultraviolet Spectrograph (IUVS) onboard the Mars Atmosphere and Volatile Evolution (MAVEN), we derive the diurnal and semidiurnal thermal tides from 90 to 160 km. The seasonal variations of diurnal (DW1) and semidiurnal (SW2) tides in the thermosphere and mesosphere, observed by the Mars Climate Sounder (MCS), along with vertical phase velocities, indicate different sources for the migrating tide in the lower and upper atmosphere. The seasonal variation of diurnal eastward wavenumber 2 (DE2) tide in the thermosphere corresponds well to its counterpart in the lower atmosphere. Vertical phase velocities indicate that the DE2 propagates upward from the lower atmosphere to ~150 km, except near the perihelion (solar longitude 210° to 270°). The upward propagation of this DE2 tide could potentially impact the vertical coupling between the Martian lower and upper atmosphere.

The cloud properties and governing processes in Southern Ocean marine boundary layer clouds have emerged as a central issue in understanding the Earth's climate sensitivity. While the simulated cloud feedbacks in Southern Ocean clouds have evolved in the most recent climate model intercomparison, the background properties of simulated summertime clouds in the Southern Ocean are not consistent with measurements due to known biases in simulating cloud condensation nuclei concentrations. This paper presents several case studies collected during the Capricorn 2 and Marcus campaigns held aboard Australian research vessels in the Austral Summer of 2018. Combining the surface-observed cases with MODIS data along forward and backward air mass trajectories, we demonstrate the evolution of cloud properties with time. These cases are consistent with multi-year statistics showing that long trajectories of air masses over the Antarctic ice sheet are critical to creating high droplet number clouds in the high latitude summer Southern Ocean. We speculate that secondary aerosol production via the oxidation of biogenically derived aerosol precursor gasses over the high actinic flux region of the high latitude ice sheets is fundamental to maintaining relatively high droplet numbers in Southern Ocean clouds during Summer.

We train random and boosted forests, two machine learning architectures based on regression trees, to emulate a physics-based parameterization of atmospheric gravity wave momentum transport. We compare the forests to a neural network benchmark, evaluating both offline errors and online performance when coupled to an atmospheric model under the present day climate and in 800 and 1200 ppm CO2 global warming scenarios. Offline, the boosted forest exhibits similar skill to the neural network, while the random forest scores significantly lower. Both forest models couple stably to the atmospheric model, and control climate integrations with the boosted forest exhibit lower biases than those with the neural network. Integrations with all three data-driven emulators successfully capture the Quasi-Biennial Oscillation (QBO) and sudden stratospheric warmings, key modes of stratospheric variability, with the boosted forest more accurate than the random forest in replicating their statistics across our range of carbon dioxide perturbations. The boosted forest and neural network capture the sign of the QBO period response to increased CO2, though both struggle with the magnitude of this response under the more extreme 1200 ppm scenario. To investigate the connection between performance in the control climate and the ability to generalize, we use techniques from interpretable machine learning to understand how the data-driven methods use physical information. We leverage this understanding to develop a retraining procedure that improves the coupled performance of the boosted forest in the control climate and under the 800 ppm CO2 scenario.

Atmospheric general circulation models (AGCMs) and coupled general circulation models (CGCMs) in the High-Resolution Model Intercomparison Project (HighResMIP) were evaluated on their ability to simulate tropical cyclone (TC) activity in the western North Pacific over its annual cycle. Specifically, we examined these models’ ability to simulate the south-north migration of mean TC genesis location. The results revealed that both types of models realistically captured TC numbers and the south-north migration of TC genesis locations in response to the annual cycle. However, TC number decreased less rapidly in the AGCMs than in both the CGCMs and observed data during the monsoon retreat period (after September). This bias was attributed to a cyclonic anomaly in the Philippine Sea in response to La Nin ̵̃a-like sea surface temperature (SST) differences between the AGCMs and the CGCMs. This cyclonic anomaly occurred when the northeasterly trade wind arose and was maintained through wind-evaporation-SST feedback.

Data-driven parameterization schemes have gained much attention in the field of atmospheric science nowadays. We design an ideal case using the Barotropic Vorticity Equation (BVE) model with periodic shear forcing and a deep learning (DL) model. The trained BVE model can greatly improve its initial forecast lead time with the data-driven parameterization scheme. However, the challenge is the significant amount of time required to employ the model, which is a hurdle for practical applications. Thus, this research aims at enhancing the model’s efficiency while minimizing any negative impact on its performance. Here we propose three methods, compressing the amount of model parameters (CNNhk), compressing the amount of training data (SGM), and combining both of them (CFS). Through these three methods and utilizing the solver-in-the-loop (SOL) method, we successfully reduce the model’s runtime while preserving its initial performance. By improving the effectiveness of our model, we believe it can contribute to the development of more efficient data-driven parameterization schemes and inspire further explorations.

Sea ice mediates the exchange of momentum, heat, and moisture between the atmosphere and the ocean. Cyclones produce strong gradients in the wind field, imparting stress into the ice and causing the ice to deform. In turn, increased sea ice drift speeds and rapid changes in drift direction during the passage of a cyclone may result in enhanced momentum flux into the upper ocean.  During the year-long MOSAiC expedition, an array of drifting buoys was deployed surrounding the R/V Polarstern, enabling the characterization of sea ice motion and deformation across a range of spatial scales. In addition, autonomous sensors at a subset of sites measured the atmospheric and oceanic structure and vertical fluxes. Here, we examine a strong cyclone that impacted the MOSAiC site during January and February, 2020, while the MOSAiC site was near the North Pole. The cyclone track intersected the MOSAiC buoy array, providing an opportunity to examine spatial variability in sea ice motion during the storm in unprecedented detail. A key feature of the storm was the formation of a low-level jet (LLJ), first in the warm sector of the storm, then growing to eventually encircle the central low. The highest rates of ice motion and deformation coincide with effects of LLJ transitions. Analysis of deformation using the Green’s theorem approach indicates divergence and cyclonic vorticity as the LLJ enters the region, and convergence and anticyclonic vorticity as the LLJ leaves; maximum shear strain rate is enhanced throughout the LLJ’s passage. While the vorticity signal is particularly clear, floe structure and internal ice stresses result in high spatial variability in the magnitude of divergence and shear strain rates, especially at smaller scales. Increased current speed and shear in the upper layer of the ocean during the passage of the LLJ resulted from ice drag forcing the ocean mixed layer current. The results suggest an important role for cyclone-forced ocean mixing in pack ice during the Arctic winter.

• A physics-informed neural network trained without ground truths can provide accurate initial fields for numerical prediction. • The system's kinetic features are embedded into the model through our four-dimensional variational form loss function. • We show on Lorenz96 that the proposed method can be used directly for accurate data assimilation at a low computational cost.