Dust aerosols play a critical role in radiative forcing, cloud microphysics, biogeochemical cycles, and air quality. Their interactions with regional climate processes in East Asia are particularly complex, necessitating a comprehensive approach to assess their spatial and temporal variability. In this study, we combine multiple sources of observations collected over a twenty-year period (2005–2024) to provide a robust analysis of dust aerosol behavior. Ground-based measurements—including the Asian Dust and Aerosol Lidar Observation Network (AD-Net) and AERONET—offer high-resolution insights into aerosol vertical profiles and optical properties. In addition, we compare these observational datasets with outputs from CMIP6 models to evaluate the performance of current climate simulations regarding dust aerosol dynamics. Our integrated analysis reveals a statistically significant decline in dust aerosol optical depth over 2005–2014, observed consistently across both ground-based datasets and largely in agreement with trends predicted by CMIP6 models. Trend for a longer time period needs further investigation. This downward trend suggests modifications to the regional energy balance that could significantly affect cloud formation, precipitation patterns, and overall climate feedback mechanisms. The study underscores the importance of improving dust aerosol representations in climate models to enhance the accuracy of future projections and deepens our understanding of aerosol–climate interactions in East Asia.

This study implements a percentile-based methodology to analyze historical drought patterns and project future conditions using Global Climate Model (GCM) simulations. By combining statistical detrending, visual inspections, and computational analysis, we identify key drought events and evaluate their severity over time. Our study area covers various climatic regions in Southern California including the Central Valley, Sierra Nevada, Mojave Desert, and Transverse Ranges. Our results highlight a significant intensification of droughts in future projections, with increasing frequency and severity surpassing historical extremes. Keywords: Drought Analysis, Global Climate Models, Python Applications

Hydroclimate change is a prominent facet of climate change, which focuses on how water and the climate interact. Climate models are used to project the future hydroclimate; however, they are imperfect, particularly for regional climate. In arid/semi-arid regions, improving hydroclimate model accuracy is significant because they are crucial for identifying areas with lower humidity that are at risk for wildfires. One proposed solution to improve climate models is to remove atmospheric circulation-induced variability in humidity using the constructed circulation analogs (CCA) method and analyze the residuals to identify breakpoints or trends in the humidity data. The CCA method finds past atmospheric circulation patterns similar to a pre-determined circulation pattern and constructs an "analog" climate signal, which is a weighted combination of the past circulation patterns that approximates the target pattern. These weights are used to estimate the dynamical component, which is then removed, leaving the residual component. To validate the method’s effectiveness, breakpoint detection methods can be applied to the data before and after removing the dynamical component to identify if a significant difference is noted. The CCA method will make identifying trends easier, which can provide insight into and improve the accuracy of climate models. By refining the models, they can be used to propose policies that address and prevent potential climate disasters such as wildfires.

Wildfire frequency across the United States since the 1980s has increased largely due to anthropogenic climate change and land management. One consequence of wildfire seasons, which are longer and more severe is increased emissions of toxic pollutants such as PM2.5, which are fine particles less than 2.5 micrometers in diameter that cause adverse respiratory and cardiovascular health outcomes. Satellite models have been used in the past to estimate emissions but have no fire data prior to the 1980s and have poorly resolved emissions estimates on scales finer than 10x10km. Using DYNAFFOREST, a forest ecology model, researchers can run simulations of fires starting from the 1950s and estimate emissions on a much finer scale than traditional satellite models. One component that can significantly change the amount of PM2.5 emitted is the type of vegetation burned. To investigate how vegetation impacts PM2.5 emissions, we used outputs from 10 replicates from a historical DYNAFFOREST simulation of wildfires from 1984 to 2023 and stochastic simulations of fires from the 1950s to 1980s. From these outputs, we calculated PM2.5 emissions for 12 different plant functional types across the Western United States from 1950 to 2023. On average, Hemlock-Cedar had the largest average PM2.5 emissions per 1-km grid cell simulated (140,000 g), whereas Five Needle Pine had the least (30,000 g). Our results will allow forest management professionals to better understand the impacts of vegetation types on PM2.5 emissions.

This abstract has been withheld from publication.

Coastal phytoplankton are important contributors to primary productivity due to high growth rates in nutrient-rich waters. Phytoplankton abundance and community composition may be the result of anthropogenic forces, including wastewater and runoff inputs. In fact, anthropogenic forces are likely leading to increased harmful algae bloom (HAB) frequency, comprising dinoflagellates (about 75% of HAB taxa) and diatoms that grow to high abundances and can produce toxins that affect birds, mammals, and humans. The Santa Monica Bay provides a unique geography, climate, and ecosystem for our study as it receives significant wastewater and runoff from nearby watersheds, with its first records of massive red tides attributed to dinoflagellate beginning in the 20th century. However, phytoplankton abundance and distributions along the coastline are often concentrated, with gaps in understanding their community composition. Samples were taken on UCLA’s research Zodiac from March through July of 2023, and phytoplankton taxonomic groups were quantified microscopically to determine phytoplankton abundance and distribution in Santa Monica Bay during one annual cycle. Data will be analyzed to evaluate onshore and offshore distribution and correlations with physical and chemical environmental factors. This project aims to identify and evaluate conditions that lead to harmful and non-harmful algal bloom events to protect local biota and beach-goers dependent on the Southern California coastal ocean waters.

Many communities in the United States are exposed to environmental hazards and threats to public health that can be further aggravated by systemic disadvantages inhibiting their representation within government. To determine how to support community participation in setting climate policy, we are working alongside community and higher education partners in Southern California to discuss community priorities and explore the potential of deliberative democratic tools. Previous research on deliberative mini-publics (DMPs) show that they increase public participation in governance by directly involving community members within the deliberation process. However, there have been no climate-related DMPs within California at this time. In order to effectively encourage knowledge sharing that transcends social boundaries, we aim to learn from community partners, students, and youth alike throughout this process. While remaining conscious of our positionality as researchers in academia, we plan to increase community awareness around the variety of DMPs available to be utilized, without limiting how one may (or may not) choose to implement the process in the future. We hope that through their discussions, these community members may find themselves more emboldened to engage in local environmental policies and climate advocacy efforts across the United States during the uncertainties of these next four years.