Dr. Jennifer Gannon passed away suddenly on 2024 May 2 in Greenbelt, MD. Dr. Gannon had served as an editor for the Space Weather journal since April 2019, and she was the longest-serving editor on the current board, having started under the previous editor-in-chief, Dr. Delores Knipp. Abstract content goes here

Cloud phase has important impacts on Arctic surface temperatures, and there is substantial circumstantial evidence that dust aerosols have strong impacts on cloud phase over the Arctic on a regional scale. We used seven years of satellite observations and model and reanalysis products to control for co-varying meteorology, and to assess how dust and other aerosols impact cloud phase over the summertime sea ice. We focus on clouds at 3 km, where dust modeling is most accurate. Dust aerosols caused about 4.5% of clouds below -15 °C to change phase, with smaller effects at higher temperatures. Sulfate has a smaller impact on cloud phase. Dust is associated with cloud-mediated surface cooling of up to a 6.3 W m-2 below single-layer clouds at ~3 km in June. Lastly, we discuss the optimal meteorological conditions for future in situ studies to maximize insight into the mechanisms driving the observed dust effects.

A document by Ron baiman. Click on the document to view its contents.

Ertel’s potential vorticity (PV) is an important quantity in atmospheric dynamics that succinctly encompasses the principles of mass, momentum, and energy conservation and is applicable to all scales of motion. In this paper, we describe the implementation of a PV diagnostics package into the atmospheric component of the Model for Prediction Across Scales (MPAS), a fully compressible nonhydrostatic global model that enables regional mesh refinement to convection-permitting resolutions and is highly suited for studies on multiscale process interactions and forecast error-growth dynamics. The version of the PV diagnostics package emphasized herein will be included in an upcoming MPAS release and significantly improves upon an original version that was introduced in MPAS v5.0. Specifically, this revised version enables the calculation of the full Eulerian PV budget at each time step Δt (i.e., the instantaneous budget) and the accumulation of PV tendencies throughout the model integration (i.e., the accumulated budget). Through the formulation of the discretized PV budget equation and global simulations conducted on a 15–3-km variable-resolution mesh, we demonstrate that the instantaneous PV budget closes down to machine roundoff in both single- and double-precisions. Further, we find that the PV budget computed using accumulated PV tendencies leads to a small source of residual that arises due to the inherent nonlinearity of PV, which leads to leads to mathematical inconsistencies between the discretized equation for calculating the PV budget over any arbitrary period longer than Δt and that which results from accumulating the PV tendencies themselves over successive time steps.

We analyze the importance of cloud top temperature, dust aerosol, sea salt aerosol, and sea ice cover for the thermodynamic phase of low-level, mid-level, and mid to low-level clouds observed by CloudSat/CALIPSO over the Arctic and the Southern Ocean using an explainable machine learning technique. As expected, the cloud top temperature is found to be the most important parameter for determining cloud phase. The results show also a predictive power of sea salt and sea ice on the phase of low-level clouds, while in mid-level clouds dust shows predictive power. Over the Southern Ocean, strong zonal winds coincide with the aerosol distribution. While they can produce high mixing ratios of sea spray at lower levels, the strong zonal winds may prevent the pole-ward transport of dust. Sea ice may prevent the release of sea salt aerosols and marine organic aerosols leading to higher liquid fractions in clouds over sea ice.

The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of coupled cloud physics and land-surface models. Here, we use functional decomposition of satellite-derived SM (SMAP/L4) and cloud vertical profiles (CVP: GPM/DPR/L2A) in the central US to quantify the relationship between SM and the vertical distribution of cloud water. High-dimensional model representation disentangles the contributions of SM and other land-surface and atmospheric variables to the CVP. Results show the sign and strength of this feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in the antecedent 7-hour SM and land-surface temperature can increase reflectivity up to 4 dBZ in the lower atmosphere (1-3 km above the surface). The presented approach brings new insights into observational understanding of SM-precipitation feedback and possesses the potential for diagnosing cloud models regarding land-atmosphere coupling representation.

Sea salt is the largest source of natural aerosol in the atmosphere by mass. Formed when ocean waves break and bubbles burst, sea salt aerosols (SSA) influence Earth’s climate via direct and indirect processes. Models participating in the sixth Coupled Model Intercomparison project (CMIP6) demonstrate a negative effective radiative forcing when SSA emissions are doubled. However, the magnitude of the effective radiative forcing ranges widely from -0.35 +/- 0.04 W/m^2 to -2.28 +/- 0.07 W/m^2, with the largest difference over the Southern Ocean. Differences in the response to doubled SSA emissions arise from model uncertainty (e.g. individual model physics, aerosol size distribution) and parameterization uncertainty (e.g. how SSA is produced in the model). Here, we perform single-model experiments with UKESM1-AMIP incorporating all of the SSA parameterizations used by the current generation of CMIP6 Earth system models. Using a fixed SSA size distribution, our experiments show that the parameterization uncertainty causes large inter-model diversity in SSA emissions in the models, particularly over the tropics and the Southern Ocean. The choice of parameterization influences the ambient aerosol size distribution, cloud condensation nuclei and cloud droplet number concentrations, and therefore direct and indirect radiative forcing. We recommend that modelling groups evaluate their SSA parameterizations and update them where necessary in preparation for future model intercomparison activities

During marine cold-air outbreaks (MCAOs), when cold polar air moves over warmer ocean, a well-recognized cloud pattern develops, with open or closed mesoscale cellular convection (MCC) at larger fetch over open water. The Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) provided a comprehensive set of ground-based in-situ and remote sensing observations of MCAOs at a coastal location in northern Norway. MCAO periods that unambiguously exhibit open or closed MCC are determined. Individual cells observed with a profiling Ka-band radar are identified using a watershed segmentation method. Using self-organizing maps (SOMs), these cells are then objectively classified based on the variability in their vertical structure. The SOM nodes contain some information about the location of the cell transect relative to the center of the MCC. This adds classification noise, requiring numerous cell transects to isolate cell dynamical information. The SOM-based classification shows that comparatively intense convection occurs only in open MCC. This convection undergoes an apparent lifecycle. Developing cells are associated with stronger updrafts, large spectrum width, larger amounts of liquid water, lower surface precipitation rates, and lower cloud tops than mature and weakening cells. The weakening of these cells is associated with the development of precipitation-induced cold pools. The SOM classification also reveals less intense convection, with a similar lifecycle. More stratiform vertical cloud structures with weak vertical motions are common during closed MCC periods and are separated into precipitating and non-precipitating stratiform cores. Convection is observed only occasionally in the closed MCC environment.

Nitric Oxide (NO) significantly impacts energy distribution and chemical processes in the mesosphere and lower thermosphere (MLT). During geomagnetic storms, a substantial influx of energy in the thermosphere leads to an increase in NO infrared emissions. Accurately predicting the radiative flux of Nitric Oxide is crucial for understanding the thermospheric energy budget, particularly during extreme space weather events. With advancements in computational techniques, machine learning (ML) has become a highly effective tool for space weather forecasting. This effort becomes even more worthwhile considering the availability of two decades of continuous NO infrared emissions measurement by TIMED/SABER, along with several other key thermospheric variables. We present the scheme of development of an ML-based predictive model for Nitric Oxide Infrared Radiative Flux (NOIRF). Various ML algorithms have been tested for better predictive ability, and an optimized model (NOEMLM) has been developed for the study of NOIRF. This model is able to extract the underlying relationships between the input features and effectively predict the NOIRF. The NOEMLM predictions have very good agreements with SABER observation during quiet time as well as geomagnetic storms. In comparison with the existing TIEGCM model, NOEMLM has very good performance, especially during extreme space weather conditions. The results of this study suggest that utilizing geomagnetic and space weather indices with ML/AI can serve as superior parameters for studying the upper atmosphere, as compared to focusing on specific species having complex chemical processes and associated uncertainties in constituents. ML techniques can effectively carry out the analysis with greater ease than traditional chemical studies.

Fa Li1,*, Qing Zhu2, Kunxiaojia Yuan2, Fujiang Ji1, Arindam Paul3, Peng Lee3, Volker C. Radeloff1, Min Chen1,*1Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, USA2Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA3America Family Insurance*Corresponding authors: Fa Li ([email protected]) and Min Chen ([email protected])

A document by Rachel Atlas. Click on the document to view its contents.

Production and use of halocarbons (halogenated hydrocarbons) saw explosive growth after the Second World War for use as refrigerants (including air conditioning and industrial refrigeration), solvents, and aerosol propellants. Within decades after their mass introduction, they were implicated in the destruction of Earth's protective ozone layer, concerns over which prompted an international effort to phase out largescale use of the most problematic halocarbon classes, namely chlorofluorocarbons (or CFCs) and hydrochlorofluorocarbons (or HCFCs), under the Montréal Protocol. However, the non-chlorinated hydrofluorocarbon (or HFC) alternatives that were developed to replace CFCs and HCFCs have proved to be exceptionally effective greenhouse agents and historically have been underreported in the ongoing discussions around climate change mitigation. Recent concerns gathering around their increasing abundance in the atmosphere have prompted increased government scrutiny and regulation, resulting in the 2016 Kigali Amendment to the Montréal Protocol recently ratified by the United States. Despite the concerted international effort currently underway to curtail the manufacture and consumption of the most environmentally impactful halocarbons, there have been several reports concerning discrepancies between measured atmospheric abundances and reported inventories of select halocarbon species that have defied expectations.

This paper takes a comprehensive look at Artificial Intelligence (AI)-based weather forecasting models and focuses on the current status, challenges, and directions for further development. A review of nearly 40 models proposed primarily after 2015 highlights the importance of critically examining various aspects of AI-based weather forecasting models, including Machine Learning (ML) techniques, datasets, predictand parameters, extreme weather forecasting capability, lead time, spatiotemporal scale, performance criteria, and in-depth analysis of state-of-the-art models from different perspectives. Unlike previous reviews that have targeted only a limited number of models or features, this study focuses on different aspects of current AI-based models. Some important characteristics of AI-based models are computational efficiency and forecasting speed. However, challenges such as limited historical data and quality, model explainability, extreme weather forecasting, physical constraints, temporal adaptation, generalization, and uncertainty remain. Addressing these challenges is essential to enhance the effectiveness and reliability of AI-based weather forecasting across different weather conditions.

Lightning flashes result from charge separation caused by colliding ice particles. They are particularly prevalent in continental convection but also occur over oceans, where their frequency correlates with maritime shipping emissions. In 2020, the International Maritime Organization (IMO) restricted ship fuels to much lower sulfur concentrations. We use this abrupt perturbation to study the response of oceanic lightning to aerosol, cloud, and convective properties. In principle, a reduction in aerosols should lead to a decrease in lightning over shiptracks relative to unpolluted regions. However, we find only one of the two investigated shipping lanes displays this sensitivity. Using the spatiotemporal sensitivity of lightning flash rate to hydrometeor size, we estimate the sensitivity of cloud water path to cloud number concentration. The results offers insights into both the role of aerosols in generating oceanic lightning and the mechanisms of aerosol-cloud interactions in deep convection.

The impact of land variables on temperature forecasts in atmospheric cycling is often underestimated or overlooked. This oversight primarily occurs due to the abundance of meteorological measurements available for assimilation and partly because soil states are assumed to be quickly reset by atmospheric forcing, such as precipitation, justifying no spin-ups or no updates of soil states during cycling. In this study, by updating soil moisture every 6 hours using different analysis datasets for May 2019, considerable discrepancies were found, highlighting large uncertainties in soil moisture analysis. Different soil moisture analyses produced systematically different temperature forecasts, with errors growing over cycles to be comparable to a typical error magnitude of 2-m temperature observations (~2ºK). This study demonstrates that temperature forecasts are significantly influenced by whether and how soil moisture is updated, not only near the surface but also up to the low-mid troposphere and throughout the cycles.

Stratospheric transit time distributions (age-of-air spectra) are estimated using satellite water vapor (H2O) measurements from the Microwave Limb Sounder over 2004-2021 assuming stationary transport. Latitude-altitude dependent spectra are derived from correlations of interannual H2O anomalies with respect to the tropical tropopause source region, fitted with an inverse Gaussian distribution function. The reconstructions accurately capture interannual H2O variability in the ‘tropical pipe’ and global lower stratosphere, regions of relatively fast transport (~1-2 years) in the Brewer-Dobson circulation. The calculations provide novel observational estimates of the corresponding ‘short transit-time’ part of the age spectrum in these regions, including the mode. However, the H2O results do not constrain the longer transit-time ‘tail’ of the age spectra, and the mean age of air and spectral widths are systematically underestimated compared to other data. We compare observational results with parallel calculations applied to the WACCM chemistry-climate model and the CLaMS chemistry-transport model, and additionally evaluate the method in CLaMS by comparing with spectra from idealized pulse tracers. Because the age spectra accurately capture H2O interannual variations originating from the tropical tropopause, they can be used to identify ‘other’ sources of variability in the lower stratosphere, and we use these calculations to quantify H2O anomalies in the Southern Hemisphere linked to the Australian New Years fires in early 2020 and the Hunga volcanic eruption in 2022.

Atmospheric rivers (ARs) play a critical role in moisture transport across the Southern Hemisphere mid-latitudes and often produce extreme rainfall events across New Zealand. Here we examine ARs in a new set of high-resolution (12km) dynamically downscaled simulations from select CMIP6 models. We begin with a historical evaluation of AR properties from this model ensemble followed by future projections. We demonstrate that by end-of-century, the maximum integrated vapour transport associated with ARs will robustly increase by as much as 20% in some regions of the South Pacific, and ARs contribute a larger proportion (up to 20% more) of the annual precipitation climatology in certain regions of New Zealand. The spatial structure and seasonality of these changes indicate the role of a poleward shift and intensification of the westerly jet. This is further quantified through a decomposition into dynamic and thermodynamic components. While the thermodynamic change dominates the increase in AR frequency, the additional positive dynamical change is notable over the mid-latitudes and southern New Zealand. Separating events using an AR categorisation scale shows that higher category (longer duration, more intense) events more than double in frequency, underscoring the increasing role that ARs will play in extreme weather events in the future in this region.

Using the Global-scale Observations of the Limb and Disk (GOLD) and the Global Ultraviolet Imager (GUVI), we examine the impact of sudden stratospheric warmings (SSWs) on the changes of thermospheric composition during the 2018-2019 and 2020-2021 Arctic SSWs and the 2019 Antarctic SSW. Contributions of planetary waves, gravity waves, and migrating tides are assessed by performing numerical experiments with the NSF National Center for Atmospheric Research (NCAR) vertically extended version of the Whole Atmosphere Community Climate Model (WACCM-X). The wind evolution simulated in WACCM-X aligns well with the quasi-geostrophic wind derived from Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) geopotential height measurements during Arctic and Antarctic SSWs. The variations in column integrated O and N2 density ratio (∑𝑂/N2) are generally similar among WACCM-X, GOLD, and GUVI observations. Following the onset of SSWs, ∑𝑂/N2 is reduced by ~10% over 50°S-50°N and enhanced by ~20% at higher latitudes. The ∑𝑂/N2 changes are associated with the reversals of the mean meridional circulation in the lower thermosphere, mainly driven by westward-travelling planetary waves. The results highlight that planetary wave activity during SSWs can significantly impact the mean state of the thermosphere.