Jimmy Chi Hung Fung

Canadian permafrost wildfires, characterized by surface flaming and deep organic soil smoldering, emit complex suites of volatile organic compounds (VOCs). These emissions include air toxics that pose substantial public health risks and fuel secondary pollution, yet remain understudied. We quantified VOC emissions from the record‐setting 2023 wildfires using three inventories (GFED, GFAS, QFED) and assessed air quality impacts via GEOS‐Chem simulations and in situ observations. VOC emissions amount to ∼15 ± 8 TgC, just ∼2% of inorganic carbon. Despite this small fraction, their impacts are significant: simulated concentrations increase by 2–5 μg m ⁻³ for benzene and 5–35 μg m ⁻³ for formaldehyde and acetaldehyde during the fire season. Organic aerosols account for ∼60%–80% of the persistent record‐high PM _2.5 during the season. These findings highlight the distinct role of high‐latitude permafrost wildfires in driving regional reactive organic carbon (ROC) pollution and provide a framework to address interdisciplinary Earth science challenges.

Urban weather/climate extremes arise from interactions between large-scale atmospheric circulation and heterogeneous urban land-atmosphere processes, yet existing models rarely represent both within a unified framework. Regional models provide detailed urban physics but rely on lateral boundary conditions, whereas global models generally lack explicit urban canopy representations and sufficient spatial resolution for city-scale applications. Here we develop MPAS–Urban, the first urban-capable implementation of the Model for Prediction Across Scales (MPAS), by coupling a single-layer urban canopy model with the Noah-MP land model on a variable-resolution global mesh from 30 km globally to 500 m over Hong Kong. This framework incorporates a global 100-m Local Climate Zone dataset to represent detailed spatially-varying urban classifications. The system is evaluated for record-breaking 2022 Hong Kong heatwave and compared with the default bulk urban representation. MPAS-Urban reproduces the large-scale atmospheric circulation associated with the event, including the Northwest Pacific Subtropical High, and captures regional distributions of near-surface temperature, humidity, and wind. At the local scale, the explicit urban canopy treatment enhances urban heat storage and aerodynamic drag, producing more realistic near-surface air temperature and substantially improved urban wind speed especially in high-density urban areas. Surface flux observations show that these improvements arise from more realistic partitioning of energy and momentum exchange within the urban canopy, although daytime humidity remains underestimated. MPAS–Urban enables seamless cross-scale modeling of interactions between synoptic forcing and local urban processes, establishing a foundation for next-generation urban weather prediction and climate assessment.

Kilometer-scale dynamical climate downscaling is increasingly important for climate-risk information to rapidly urbanizing regions such as Southeast Asia (SEA), where megacities are vulnerable to extreme rainfall, heat stress, and urban climate hazards. However, the extent to which explicit urban representation improves convection-permitting regional climate simulations remains insufficiently quantified. Here, we developed and evaluated a 4-km convection-permitting Weather Research and Forecasting (WRF) modeling framework over SEA to investigate the sensitivity of precipitation, near-surface temperature, and surface wind to urban land-use representation and urban canopy parameterization. Sensitivity experiments were conducted using MODIS urban land use and high-resolution Local Climate Zone (LCZ) datasets with Bulk, SLUCM, BEP, and BEPBEM schemes. Urban drag parameterization and rural surface wind diagnosis were modified to address persistent surface wind-speed biases.Simulations were compared to ERA5 and evaluated against station observations in five metropolitan regions: the Pearl River Delta (PRD), Bangkok, Manila, Kuala Lumpur, and Jakarta. Results show urban representation influences variables despite convection-permitting resolution. LCZ better represents urban extent and morphology than MODIS by improving thermal patterns. Incorporating multilayer urban canopy physics, particularly BEPBEM, improves heat island intensity and temperature, increases heavy-precipitation frequency and better captures precipitation extremes. Modified urban drag and rural wind diagnosis reduce wind overestimation. Although the short evaluation period limits climatological generalization, the results provide physically consistent evidence that urban morphology and canopy processes remain important for kilometer-scale modeling of SEA megacities. The combination of LCZ, BEPBEM, and modified drag treatment provides the most balanced performance and supports long-term convection-permitting downscaling over SEA.

Climate change brings emerging complex risks, subtle and weak, starting to manifest in some regions around the world, followed by the recurrence of preventable tragedies across regions. For instance, in Macau in 2017, people drowned in flooded underground car parks as they tried to save their vehicles. Tragically, similar preventable tragedies have since recurred in South Korea (2022) and Spain (2024). Before these incidents, local disaster risk reduction strategies in Macau, South Korea, and Spain did not cover specific guidelines addressing the resilience of underground spaces to extreme weather. Although local governments eventually enhance their regulations, such action is typically a reactive measure, triggered only by catastrophe rather than proactive foresight.The primary obstacle to foresight is the challenge of identifying ‘unknown unknowns’—rare, variable-severity emerging risks. Our study directly addresses this critical gap in the early warning chain by demonstrating a systematic methodology that leverages cross-regional knowledge of analogous events to identify ‘unknown unknowns’ for regions without prior experience, thereby transforming them into foreseeable risks and enabling proactive preparation and strengthening response capabilities.This study utilizes Natural Language Processing to analyze 7.7 million news articles across four dimensions—public awareness, priority of human needs, level of severity, and scope of influence—identifying 639 emerging climate threats, subsequently refined by an expert intervention to pinpoint the most critical tail-end risks. The findings uncover a wide spectrum of lesser-known emerging risks across diverse sectors, such as health, food, infrastructure, finance, transportation, and wildlife-related threats. An example of the findings is a paradox, first identified in peer-reviewed research and subsequently reported by the media. This paradox reveals that mercury in fish is increasing even as oceanic mercury declines, a phenomenon driven by warmer seawater that compels fish to migrate to cooler regions, which in turn elevates their energy consumption and accelerates bioaccumulation.Ultimately, this research provides a practical decision-support tool for a range of stakeholders. By translating ‘unknown unknowns’ into actionable insights, our methodology enables a paradigm shift from reactive post-disaster response to proactive risk management. Specifically, these identified risks can be used to inform targeted risk communication strategies and establish triggers for anticipatory action. This provides a crucial component for the UN’s ‘Early Warnings for All’ (EW4All) initiative, enabling communities and disaster managers to prepare for emerging complex risks before they manifest as localized crises.

Extreme wind and precipitation events result in signicant societal disruption in the South China coastal region, typically triggered by tropical cyclones (TCs) or mesoscale storms. The large-, meso-, and small-scale atmospheric circulation processes that can influence these high- impact weather events may be altered by climate change, potentially changing TC characteristics. However, quantifying the sensitivity of TCs and extreme precipitation to climate change is challenging, primarily due to the limited detail provided by global model simulations with coarse resolution. High-resolution simulations are essential to address such issues. We have developed a smart dynamical downscaling (SDD) model to downscale the climate simulations (100 km) to high-resolution simulations (15 km). The trained SDD model can be applied to ensemble climate simulations under the SSP585 scenario from 2020 to 2100 to explore the variations of the severe TC cases, regarding the spatial distribution, maximum surface wind, and precipitation, respond to global warming. We found the inland areas of China will be affected more by TC-induced extreme precipitation and the intense typhoons are increased in the future based on the ensemble downscale results. The high-resolution simulations are conducted for selected extreme precipitation events to further under the dynamical response to global warming.

Volatile organic compounds (VOCs) are a diverse group of species that contribute to ozone formation. However, our understanding of VOC dynamics and their effect on ozone pollution is limited by the lack of long-term, continuous, and speciated measurements, especially of oxygenated compounds. To address this gap, this study integrates on-land, shipborne, and spaceborne measurements from a field campaign in Hong Kong during 2021–2022, analyzing 45–98 VOC species over land and water. Results show that oxygenated VOCs (OVOCs) account for 73 % (37 ppbv) of the total VOC concentration and 56 % of the total ozone formation potential (OFP), underscoring their indispensable role in VOC chemistry. Despite such importance, OVOCs are underestimated by 45 %–70 % in the CMAQ model, while non-methane hydrocarbons (NMHCs) face a lesser underestimation of 47 %–48 % (i.e., “model underestimation”). Meanwhile, the model does not currently account for 17–56 species of the total measured VOCs (i.e., “model omission”). According to this, we break down the observed overwater VOC concentration of 51 ppbv into three components: 9 ppbv (18 %) successfully represented, 35 ppbv (69 %) underestimated, and 7 ppbv (14 %) omitted in the model. For OFP, the breakdown shows 26 % successful representation, 54 % underestimation, and 20 % omission. Together, both “omission” and “underestimation” reveal the overall “VOC underrepresentation” in the model, which partly results in greater ozone sensitivity to VOCs than observed by spaceborne TROPOspheric Monitoring Instrument (TROPOMI) in polluted areas. The findings provide valuable insights into regional pollution dynamics, and inform VOC-related model development and air quality management.

Understanding and modeling the turbulent transport of surface layer fluxes are essential for numerical weather forecasting models. The presence of heterogeneous surface obstacles (buildings) that have dimensions comparable to the model vertical resolution requires further complexity and design in the planetary boundary layer (PBL) scheme. In this study, we develop a numerical method to couple a recently validated PBL scheme, TKE-ACM2, with multi-layer Building Effect Parameterization (BEP) in the Weather Research and Forecasting (WRF) model. Subsequently, the performance of TKE-ACM2+BEP is examined under idealized convective atmospheric conditions with a simplified building layout. Furthermore, its reproducibility is benchmarked with a state-of-the-art large-eddy simulation model, PALM, which explicitly resolves the building aerodynamics. The results indicate that TKE-ACM2+BEP outperforms another operational PBL scheme (Boulac) coupled with BEP by reducing bias in both the potential temperature (θ) and wind speed (u). Following this, real case simulations are conducted for a highly urbanized domain, namely the Pearl River Delta (PRD) region in China. High-resolution wind speed LiDAR observations suggest that TKE-ACM2+BEP reduces overestimation in the lower part of the boundary layer compared with the Bulk method, which lacks an urban scheme, at a LiDAR site located in a densely built environment. In addition, the surface temperature and relative humidity given by TKE-ACM2+BEP at surface stations in urbanized areas are more accurate than those given by TKE-ACM2 without BEP. However, it is revealed that BEP does not always improve the accuracy of the surface wind speed, as it can introduce excessive aerodynamic drag.