Africa is increasingly being exposed to the negative impacts of climate and environmental change, while having less capacity to respond compared to other continents. The vulnerability partially results from unprecedented demographic growth, urbanization, and industrialization. However, the continent has still largely been underserved by the broader Earth system science (ESS) community, as evidenced by the limited amount of ESS data and research that cover Africa compared to other areas of the world. Here, we present the recent University Corporation for Atmospheric Research (UCAR) Africa Initiative that aims to enhance environmental sustainability in Africa by fostering international collaborative research partnerships coled by African scientists. Specifically, we outline urgent challenges and opportunities identified through an international workshop in six areas of ESS, namely, 1) air quality and health, 2) weather, 3) climate, 4) land and water, 5) social science perspectives, and 6) developing equitable collaboration and sustainable infrastructure. We highlight examples of successful partnerships and conclude with recommendations to advance collaborative, actionable ESS research that addresses Africa's critical environmental challenges.
Coastal cities face critical challenges in managing ozone pollution because of high population density and complex interactions between urbanization and coastal meteorology. Using New York City (NYC) as a case study, this work examines how urban surfaces influence heatwaves, sea-breeze circulations, and boundary-layer dynamics, and how these processes shape nitrogen dioxide () distributions and ozone formation. Observations from the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS), collected with an airborne high-resolution ultraviolet-visible spectrometer, provide tropospheric column densities for evaluating the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in multiple urban canopy configurations. Results show that the early-morning urban heat island effect accelerated sea-breeze initiation, concentrating over the city. Building-induced drag reduced wind speeds, altered advection patterns, and prolonged precursor residence times, enhancing photochemistry. These processes elevated ozone both within NYC and downwind. The findings demonstrate how the interplay of urban heat islands, building drag, and coastal circulations governs ozone pollution in coastal cities, providing a framework for improving air-quality modeling and mitigation strategies.
Graduate research programs aim to cultivate students' abilities to conduct scientific research, including theory development and application, research design, data analysis, and transferable skills like collaboration and communication. Historically, social science training has emphasized individual rather than team-based work. This may limit knowledge creation and negatively impact student confidence and overall mental health. With a growing emphasis on team science and calls for expanded and focused non-academic career preparation, the role for social science laboratories for graduate training remains underexplored. This paper uses an expert panel approach to identify specific tasks and mentorship strategies for engaging graduate students in social science labs and identifies spaces of untapped potential for fostering research skills, personal growth, and career readiness through team-based research.
Administrative burden describes the learning costs, psychological costs, and compliance costs people face when attempting to interface with the government, particularly in seeking a benefit. Algorithmic and automated processes offer the potential of reducing administrative burdens, but scant empirical research has determined to what, if any effect. This study uses the case of criminal record expungement in two policy contexts: traditional, court petition-based systems and newly enacted automated systems, to understand if and how administrative burden persists, and whether and how these burdens operate differently in the context of the criminal legal system. Drawing on interviews with 105 expungement-eligible people, we find that while automated expungement schemes shift the burden from petitioner to state to initiate the process, automation inadvertently creates new administrative burdens via failure to notify, partial clearances, and opaque data processes. Furthermore, respondents described how automation failed to provide a sense of confirmation from the state that their sentence was truly completed, rehabilitation had been acknowledged, or that collateral consequences should no longer wield the same power. Overall, we argue that leveraging automation to reduce burdens must include information availability by design; otherwise policy reforms may fail to fully achieve their goals.
Cloud phase partition among liquid, mixed, and ice phases was investigated using in situ and satellite observations. A large in situ observation dataset was compiled from 11 flight campaigns covering a near global extent (87 degrees N-75 degrees S, 128 degrees E-37 degrees W). Random forest ensemble models were used to quantify the effects of key controlling factors on cloud phase partition, including temperature (T), relative humidity with respect to ice (RHi), vertical velocity (w), and aerosol number concentrations (Na) for larger and smaller particles. Our results show that using RHi or T as a single predictor significantly enhanced the prediction accuracy of total cloud occurrences and cloud phase partition, respectively. The inclusion of Na predictors provided a slight but consistent improvement in the F1 score. The feature importance analysis revealed that Na predictors were used by the model nodes at a frequency comparable to that of w. The models successfully captured key physical features, such as the increase in ice phase frequency with decreasing temperature and the peak in-cloud frequency near ice saturation. They also reproduced the main biases in satellite products, such as the overestimation of mixed phase and ice phase by CloudSat and radar-lidar (DARDAR), respectively. These results demonstrate the utility of machine learning for quantifying the contributions of complex factors governing cloud phase partition and for diagnosing systematic biases in observation datasets.
