Marine cold-air outbreaks, or CAOs, are airmass transformations whereby relatively cold boundary layer (BL) air is transported over relatively warm water. Such convectively-driven conditions are rather ubiquitous in the high-latitudes, occurring most frequently during the winter and spring. To more deeply understand BL and cloud properties during CAO conditions, the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) took place from late 2019 into early 2020. During COMBLE, the U.S. Department of Energy (DOE) first Atmospheric Radiation Measurement Mobile Facility (AMF1) was deployed to Andenes, Norway, far downstream (~1000 km) from the Arctic pack ice. This study examines the two most intense CAOs sampled at the AMF1 site. The observed BL structures are open cellular in nature with high (~3-5 km) and cold (-30 to -50 oC) cloud tops, and they often have pockets of high liquid water paths (LWPs; up to ~1000 g m-2) associated with strong updrafts and enhanced turbulence. We use a high-resolution mesoscale model to explore how well four different turbulence closure methods represent open cellular cloud properties. After applying a radar simulator to the model outputs for direct evaluation, we show that cloud top properties agree well with AMF1 observations (within ~10%), but radar reflectivity and LWP agreement is more variable. The eddy-diffusivity/mass-flux approach produces the deepest cloud layer and therefore the largest and most coherent cellular structures. Our results suggest that the turbulent Prandtl number may play an important role for the simulated BL and cloud properties.

More than a third of the world's population lives in drylands and is disproportionally at risk of hydrometeorological hazards such as drought and flooding. While existing studies have widely explored weather systems governing precipitation formation in humid regions, our understanding of the atmospheric processes generating precipitation in arid regions remains fragmented at best. Here we show, using a variety of precipitation datasets, that Rossby wave breaking is a key atmospheric driver of precipitation in arid regions worldwide. Rossby wave breaking contributes up to 90% of daily precipitation extremes and up to 80% of total precipitation amounts in arid regions equatorward and downstream of the midlatitude storm tracks. The relevance of Rossby wave breaking for precipitation increases with increasing land aridity. Contributions of wave breaking to precipitation dominate in the poleward and westward portions of arid subtropical regions during the cool season. Given the projected precipitation decline and the large uncertainty in projections of precipitation extremes in these regions, our findings imply that Rossby wave breaking plays a crucial role in projections and uncertainties of future precipitation changes in societally vulnerable regions that are exposed to both freshwater shortages and flood hazards.

The ground-based, high-frequency radars of the Super Dual Auroral Radar Network (SuperDARN) observe backscatter from ionospheric field-aligned plasma irregularities and features on the Earth’s surface out to ranges of several thousand kilometers via over-the-horizon propagation of transmitted radio waves. Interferometric techniques can be applied to the received signals at the primary and secondary antenna arrays to measure the vertical angle of arrival, or elevation angle, for more accurate geolocation of SuperDARN observations. Calibration of SuperDARN interferometer measurements however remains challenging for several reasons, including a 2$\pi$ ambiguity in the phase correction factor needed to account for differences in the electrical path lengths between signals received at the two antenna arrays. We present a new technique using multi-frequency ionospheric and ground backscatter observations for the calibration of SuperDARN interferometer data, and demonstrate its application to both historical and recent data.

The study presents (a) a 44-year wintertime climatology of resolved gravity wave (GW) fluxes and associated zonal forcing in the extratropical stratosphere using ERA5, and (b) their composite evolution around gradual (final warming) and abrupt (sudden warming) transitions in the wintertime circulation. The connection between transformed Eulerian mean (TEM) equations and the linear GW pseudomomentum is leveraged to provide a glimpse of the importance of GW lateral propagation toward driving the wintertime stratospheric circulation by analyzing the relative contribution of the vertical vs. meridional flux convergence. The relative contribution from lateral propagation is found to be notable, especially in the Austral winter stratosphere where lateral (vertical) momentum flux convergence provides a peak climatological forcing of up to -0.5 (-3.5) m/s/day around 60oS at 40-45 km altitude. Prominent lateral propagation in the wintertime midlatitudes also contributes to the formation of a belt of GW activity in both hemispheres.

A document by Zachary Johnson. Click on the document to view its contents.

Urban Land Surface Models (ULSMs) simulate energy and water exchanges between the urban surface and atmosphere. When part of numerical weather prediction, ULSMs provide a lower boundary for the atmosphere and improve the applicability of model results in the urban environment compared with non-urban land surface models. However, earlier systematic ULSM comparison projects assessed the energy balance but ignored the water balance which is coupled to the energy balance. Here, we analyze the water balance representation in 19 ULSMs participating in the Urban-PLUMBER project using results for 20 sites spread across a range of climates and urban form characteristics. As observations for most water fluxes are unavailable, we examine the water balance closure, flux timing, and magnitude with a score derived from seven indicators. We find that the water budget is only closed in 57% of the model-site combinations assuming closure when annual total incoming fluxes (precipitation and irrigation) fluxes are within 3% of the outgoing (all other) fluxes. Results show the timing is better captured than magnitude. No ULSM has passed all good water balance indicators for any site. Our results indicate models could be improved by explicitly verifying water balance closure and revising runoff parameterizations. By expanding ULSM evaluation to the water balance and related to latent heat flux performance, we demonstrate the benefits of evaluating processes with direct feedback mechanisms to the processes of interest.

A document by Tatiana Smirnova. Click on the document to view its contents.

J. Wei and Z. Wang made equal contributions to this work.*Corresponding authors:[email protected]; [email protected]; [email protected] imagery offers remarkable potential for various applications, including land monitoring and environmental assessment, thanks to its high spatial resolution and over 50 years of data records. However, the presence of atmospheric aerosols greatly hinders the precision of land classification and the quantitative retrieval of surface parameters. Notably, there has been no global retrieval of aerosol optical depth (AOD) from Landsat imagery that is needed for atmospheric correction, among other applications. To address this issue, this paper presents an innovative global AOD retrieval framework for Landsat imagery, propelled by atmospheric radiative transfer (ART) and enhanced GeoChronoTransformers (GCT) models incorporating multidimensional spatiotemporal sequence information and executed on the Google Earth Engine (GEE) cloud platform. We gathered all Landsat 8 and 9 images from their respective launch dates (February 2013 and September 2021) up to 2022, which were used to construct a robust ART-GCT-GEE model, and then rigorously validated the model performance across ~470 monitoring stations over land using diverse spatiotemporally independent methods. Leveraging information from multiple spectral channels, contributing to 58% according to the SHapley Additive exPlanation (SHAP) method, our results are highly consistent with observations (e.g., correlation coefficient = 0.863 and root-mean-square error = 0.096), suggesting that accurate historical and future AOD levels can be obtained. Around 81% and 50% of our AOD predictions meet the criteria of Moderate Resolution Imaging Spectroradiometer (MODIS) expected errors [±(0.05+20%)] and the Global Climate Observation System {[max(0.03, 10%)]}, respectively. Additionally, our model is less influenced by changes in surface conditions like topography and land cover. This allows us to generate spatially continuous AOD distributions with highly detailed and fine-scale information from dark to bright surfaces, especially for densely populated urban areas and expansive deserts with high aerosol loadings from both anthropogenic and natural sources.

There is a lack of diversity amongst geoscience faculty. Therefore, many geoscience departments are taking steps to recruit and retain faculty from underrepresented groups. Here, we interview 19 geoscientists who identify as a member of an underrepresented race or gender who declined a tenure-track faculty job offer to investigate the factors influencing their decision. We find a range of key factors that influenced their decisions to accept or decline a position, including fit and resources, experiences during job interviews, negotiations and offers, family, geographic preferences, attention to DEI, personal identities, mentorship, hiring process, and teaching responsibilities. Despite existing recommendations for interventions to improve faculty diversity, many of the participants experienced hiring processes that did not follow these suggested best practices, suggesting that departments are not all aware of best hiring practices. Therefore, we leverage our results to provide actionable recommendations for improving the equity and effectiveness of faculty recruitment efforts. We find that institutions may doubly benefit from improving their culture: in addition to benefiting current members of the institution, it may also help with recruitment.

Earth System Models (ESMs) are essential tools for understanding the interaction of the human and Earth systems. One key application of these models is studying extreme weather events, such as heat waves or high intensity precipitation events, which have significant socioeconomic consequences. However, the computational demands of running a sufficient number of simulations to robustly characterize expected changes in these hazards, and therefore provide a strong basis to analyze the ensuing risks, are often prohibitive. In this paper we demonstrate that diffusion models – a class of generative deep learning models – can effectively emulate the spatio-temporal trends of ESM daily output. Trained on a handful of runs, reflecting a wide range of radiative forcings, our DiffESM model takes monthly mean precipitation or temperature as input and is capable of producing daily values of temperature and precipitation that have statistical characteristics close to the ESM output. This approach requires only a small fraction of the computational resources that would be needed to run a large ensemble under any scenario of interest. We evaluate model behavior over a range of scenarios, time horizons and two ESMs, using a number of extreme metrics, including ones that have been long established in the climate modeling and analysis community. Our results show that the samples produced by DiffESM closely matches the spatio-temporal behavior of the ESM output it emulates in terms of the frequency and spatial characteristics of phenomena such as heat waves, dry spells, or rainfall intensity.

Ionospheric refraction introduces significant delay and fading in the electromagnetic signals. This makes the ionosphere the most harmful layer of the Earth’s atmosphere to the electromagnetic signals emitted by satellites, impacting the reliability of GNSS services. Depending on the ionization level of the ionosphere plasma and the signal frequency, these errors can vary from a few meters to signal unavailability. The main factors influencing ionosphere plasma’s ionization level are the intensity of solar radiation and the Earth’s magnetic field. The main parameter to evaluate the behavior of the ionosphere is the Total Electron Content (TEC), existing between the satellite and the terrestrial receiver antenna. By predicting the TEC value, it is possible to predict the effects of ionospheric refraction and develop techniques to increase reliability in services that depend on GNSS. This study spans the four seasons from 2018 to 2023, utilizing measurements of ionospheric delays collected by the UTC(INXE). Daily, seasonal, and annual variations in Vertical TEC (VTEC) values are analyzed. A comparative assessment is made between the VTEC values obtained by the GPS P3 method and the Ionospheric Map method for each season until winter 2023. The Analysis of Variance demonstrated the compatibility and comparability of the two methods. Additionally, this investigation explores changes in the ionosphere behavior at the UTC(INXE) location during the geomagnetic storms caused by the solar explosions on April 21, 2023. The findings provide valuable insights for the ionosphere dynamics and can contribute to developing techniques to improve GNSS services’ reliability.

The widely-used Noah-MP land surface model (LSM) currently adopts snow albedo parameterizations that are semi-physical in nature with nontrivial uncertainties. To improve physical representations of snow albedo processes, a state-of-the-art snowpack radiative transfer model, the latest version of Snow, Ice, and Aerosol Radiative (SNICAR) model, is integrated into Noah-MP in this study. The coupled Noah-MP/SNICAR represents snow grain properties (e.g., shape and size), snow aging, and physics-based snow-aerosol-radiation interaction processes. We compare Noah-MP simulations employing the SNICAR scheme and the default semi-physical Biosphere-Atmosphere Transfer Scheme (BATS) against in-situ snow albedo observations at three Rocky Mountain field stations. The agreement between simulated and in-situ observed ground snow albedo in the broadband, visible, and near-infrared spectra is enhanced in Noah-MP/SNICAR simulations relative to Noah-MP/BATS simulations. The SNICAR scheme improves the temporal variability of modeled broadband snow albedo, with a nearly twofold higher correlation with observations (r=0.66) than the default BATS snow albedo scheme (r=0.37). The underestimated variability in Noah-MP/BATS is a result of inadequate physical linkage between snow albedo and environmental/snowpack conditions, which is substantially improved by the SNICAR scheme. Importantly, the Noah-MP/SNICAR model, with constraints of snow grain size from the MODIS snow covered area and grain size (MODSCAG) satellite data, physically represents and quantifies the snow albedo and absorption of shortwave radiation in response to snow grain size, non-spherical snow shapes, and light-absorbing particles (LAPs). The coupling framework of the Noah-MP/SNICAR model provides a means to reduce the bias in simulating snow albedo.

The interactions of clouds with radiation influence climate. Many of these impacts appear to be related to the radiative heating and cooling from high-level clouds in the upper troposphere, but few studies have explicitly tested this. Here, we use simulations with the ICON-ESM global atmosphere model to understand how high-level clouds through their radiative heating and cooling of the atmosphere, influence the large-scale atmospheric circulation and precipitation in the present-day climate. We introduce a new method to diagnose the radiative heating of high-level clouds: we use a temperature threshold of -35°C to define high-level clouds and also include the lower parts of these clouds at warmer temperatures. The inclusion of the lower cloud parts circumvents the creation of artificial cloud boundaries and strong artificial radiative heating at the temperature threshold. To isolate the impact of high-level clouds, we analyze simulations with active cloud-radiative heating, with the radiative heating from high-level clouds set to zero, and with the radiative heating from all clouds set to zero. We show that the radiative interactions of high-level clouds warm the troposphere and strengthen the eddy-driven jet streams, but have no impact on the strength of the Hadley circulation and the latitude of the Intertropical Convergence Zone. Consistent with their positive radiative heating and energetic arguments, high-level clouds reduce precipitation throughout the tropics and lower midlatitudes. Overall, our results confirm that the radiative interactions of high-level clouds have important impacts on climate and highlight the need for better representing their radiative interactions in models.

A document by Xi Cao. Click on the document to view its contents.

Exposure to ionizing radiation from galactic cosmic rays (GCR) and solar energetic particles (SEP) at aircraft flight altitudes can have an adverse effect on human health. Although airline crews are classified as radiation workers by the International Commission on Radiological Protection (ICRP), in most countries, their level of exposure is unquantified and undocumented throughout the duration of their career. As such, there is a need to assess pilot ionizing radiation exposure. The Nowcast of Aerospace Ionizing RAdiation System (NAIRAS), a real-time, global, physics-based model is used to assess such exposure. The Automated Radiation Measurements for Aerospace Safety (ARMAS) measurement dataset consists of high latitude, high altitude, and long-duration aircraft flights between 2013-2023. Here, we characterize radiation exposure at aviation flight altitudes using the NAIRAS model and compare with 45 flight trajectories from the recent ARMAS flight measurement inventory.

Nitrous oxide (N2O) is the most important stratospheric ozone-depleting agent based on current emissions and the third largest contributor to increased net radiative forcing. Increases in atmospheric N2O have been attributed primarily to enhanced soil N2O emissions. Critically, contributions from soils in the Northern High Latitudes (NHL, >50°N) remain poorly quantified despite their vulnerability to permafrost thawing induced by climate change. An ensemble of six terrestrial biosphere models suggests NHL soil N2O emissions doubled since the preindustrial 1860s, increasing on average by 2.0±1.0 Gg N yr-1 (p<0.01). This trend reversed after the 1980s because of reduced nitrogen fertilizer application in non-permafrost regions and increased plant growth due to CO2 fertilization suppressed emissions. However, permafrost soil N2O emissions continued increasing attributable to climate warming; the interaction of climate warming and increasing CO2 concentrations on nitrogen and carbon cycling will determine future trends in NHL soil N2O emissions.

Reliable air temperature forecasts are necessary for mitigating the effects of droughts and Heatwaves. The numerical weather prediction(NWP) model forecasts have significant biases associated and therefore need post-processing. Post-processing of temperature forecasts using probabilistic approaches are lacking in India. In this study, we post-process the Global Ensemble Forecast System (GEFS) and EuropeanCentre for Medium Range Weather Forecasts (ECMWF) NWP model temperature forecasts for short to medium range time scales (1-7 days)using two probabilistic techniques, namely, Bayesian model averaging(BMA) and Nonhomogeneous gaussian regression (NGR). The post-processing techniques are evaluated for temperature (maximum and minimum) predictions across the Indian region. Results show that the probabilistic approaches considerably enhance the temperature predictions across India except the Himalayan regions. These techniques also comprehensively outperform the traditional post-processing techniques such as the running mean and simple linear regression. The NGR performs better than the BMA across all regions and is able to provide highly skillful temperature forecasts at higher lead times as well. Further, the study also assesses the implication of probabilistic post-processing Tmax forecast towards forecast enhancement of heatwaves (HW) in India. Post-processed Tmax forecasts revealed that the NGR approach considerably enhanced the HW prediction skill in India, especially in the northwestern and central Indian regions, considered highly prone to HW. The findings of this study will be useful in developing enhanced HW early warning and prediction systems in India.

• A galactic cosmic ray cutoff rigidity map for Jupiter was made using the JRM33 model and the Geomagnetic Cutoff Rigidity Computer Program • The flux of galactic cosmic ray protons into Jupiter's atmosphere was calculated based on BESS-Polar ii data • Detections of heavy ions by Juno's SRU were investigated and used to estimate their equatorial pitch angles

The January 2022 eruption of Hunga Tonga-Hunga Ha’apai (HTHH) injected a huge amount (~150 Tg) of water vapour (H2O) into the stratosphere, along with small amount of SO2. An off-line 3-D chemical transport model (CTM) successfully reproduces the spread of the injected H2O through October 2023 as observed by the Microwave Limb Sounder (MLS). Dehydration in the 2023 Antarctic polar vortex caused the first substantial (~20 Tg) removal of HTHH H2O from the stratosphere. The CTM indicates that this process will dominate removal of HTHH H2O for the coming years, giving an overall e-folding timescale of 4 years; around 25 Tg of the injected H2O is predicted to still remain in the stratosphere by 2030. Following relatively low Antarctic column ozone in midwinter 2023 due to transport effects, additional springtime depletion due to H2O-related chemistry was small and maximised at the vortex edge (10 DU in column).

Arctic permafrost thaw holds the potential to drastically alter the Earth’s surface in Northern high latitudes. We utilize high-resolution Large Eddy Simulations to investigate the impact of the changing surfaces onto the neutrally stratified Atmospheric Boundary Layer (ABL). A stochastic surface model based on Gaussian Random Fields modeling typical permafrost landscapes is established in terms of two land cover classes: grass land and open water bodies, which exhibit different surface roughness length and surface sensible heat flux. A set of experiments is conducted where two parameters, the lake areal fraction and the surface correlation length, are varied to study the sensitivity of the boundary layer with respect to surface heterogeneity. Our key findings from the simulations are the following: The lake areal fraction has a substantial impact on the aggregated sensible heat flux at the blending height. The larger the lake areal fraction, the smaller the sensible heat flux. This result gives rise to a potential feedback mechanism. When the Arctic dries due to climate heating, the interaction with the ABL may accelerate permafrost thaw. Furthermore, the blending height shows significant dependency on the correlation length of the surface features. A longer surface correlation length causes an increased blending height. This finding is of relevance for land surface models concerned with Arctic permafrost as they usually do not consider a heterogeneity metric comparable to the surface correlation length.

Mesoscale convective systems (MCSs) play an important role in modulating the global hydrological cycle, general circulation, and radiative energy budget. In this study, we evaluate MCS simulations in the second version of U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv2). E3SMv2 atmosphere model (EAMv2) is run at the uniform 0.25° horizontal resolution. We track MCSs consistently in the model and observations using the PyFLEXTRKR algorithm, which defines MCS based on both cloud-top brightness temperature (Tb) and surface precipitation. Results from using Tb only to define MCS, commonly used in previous studies, are also discussed. Furthermore, sensitivity experiments are performed to examine the impact of new cloud and convection parameterizations developed for EAMv3 on simulated MCSs. Our results show that EAMv2 simulated MCS precipitation is largely underestimated in the tropics and contiguous United States. This is mainly attributed to the underestimated precipitation intensity in EAMv2. In contrast, the simulated MCS frequency becomes more comparable to observations if MCSs are defined only based on cloud-top Tb. The Tb-based MCS tracking method, however, includes many cloud systems with very weak precipitation which conflicts with the MCS definition. This result illustrates the importance of accounting for precipitation in evaluating simulated MCSs. We also find that the new physics parameterizations help increase the relative contribution of convective precipitation to total precipitation in the tropics, but the simulated MCS properties are overall not significantly improved. This suggests that simulating MCSs will remain a challenge for the next version of E3SM.

Satellites with dual-frequency Global Navigation Satellite Systems (GNSS) receivers can measure integrated electron density, known as slant Total Electron Content (sTEC), between the receiver and transmitter. Precise relative variations of sTEC are achievable using phase measurements on L1 and L2 frequencies, yielding around 0.1 TECU or better. However, CubeSats like Spire LEMUR, with simpler setups and code noise in the order of several meters, face limitations in absolute accuracy. Their relative accuracy, determined by phase observations, remains in the range of 0.1-0.3 TECU. With a substantial number of observations and comprehensive coverage of lines of sight between Low Earth Orbit (LEO) and GNSS satellites, global electron density can be reconstructed from sTEC measurements. Utilizing 27 satellites from various missions, including Swarm, GRACE-FO, Jason-3, Sentinel 1/2/3, COSMIC-2, and Spire CubeSats, a cubic B-spline expansion in magnetic latitude, magnetic local time, and altitude is employed to model the logarithmic electron density. Hourly snapshots of the three-dimensional electron density are generated, adjusting the model parameters through non-linear least-squares based on sTEC observations. Results demonstrate that Spire significantly enhances estimates, showcasing exceptional agreement with in situ observations from Swarm and Defense Meteorological Satellite Program (DMSP). The model outperforms contemporary climatological models, such as International Reference Ionosphere (IRI)-2020 and the neural network-based NET model. Validation efforts include comparisons with ground-based slant TEC measurements, space-based vertical TEC from Jason-3 altimetry, and global TEC maps from the Center for Orbit Determination in Europe (CODE) and the German Research Center for Geosciences (GFZ).

Recent research has described a ‘moist margin’ in the tropics, defined through a total column water vapor (TCWV) value of 48 kg m-2, that encloses most of the rainfall over the tropical oceans. Diagnosing the moist margin in the ERA5 reanalysis reveals that it varies particularly on synoptic time scales, which this study aims to quantify. We define ‘wet and dry perturbation’ objects based on the margin’s movement relative to its seasonal climatology. These perturbations are associated with a variety of features, such as tropical cyclones and lows, tropical waves, and extrusions of moisture towards the extratropics. Wet (dry) perturbations produce substantially more (less) rainfall compared to the seasonal average, confirming the clear link between moisture and precipitation. On synoptic scales we suggest that mid-tropospheric humidity plays a key role in creating these perturbations, while sea surface temperatures (SSTs) are relatively unimportant.

This study utilizes radar, sounding observations, and convective-permitting simulations with a non-hydrostatic mesoscale model to investigate the effects of gravity waves originating from the southwest mountain on the intensification of the extreme precipitation event occurred in Henan Province, Central China, in July 2021 (referred to as the “21.7” event). The gravity waves have wave speeds of approximately 11.5 m s-1 and wavelengths ranging from 60 to 90 km. These gravity waves are generated by the interaction between a northwest-southeast direction mountain (Funiu Mountain, FNM) and a southwesterly flow originated from the mesoscale convective vortex (MCV) developing from an inverted trough southwest of the rainfall center. Then, these waves propagate northeastward through a wave duct featuring a stable layer between 5 and 9 km altitude, capped by a low-stability reflecting layer with a critical level. As they propagate, these waves trigger banded convective cells along their path. Upon the arrival of gravity wave peaks at the rainfall center, they induce the downward energy flux of gravity waves from high troposphere levels (~7 km). The downward wave energy dynamically interacts with the upward wave energy from gravity waves excited by latent heating at the lower tropospheric level (~1 km). This synergistic effect intensifies the ascending motion and results in a precipitation increase of over 20% at the rainfall center. This study highlights the significance of orographic gravity waves in shaping extreme precipitation events.