Accurate and reliable reservoir inflow forecast is instrumental to the efficient operation of the hydroelectric power systems. It has been discovered that natural and anthropogenic aerosols have a great influence on meteorological variables such as temperature, snow water equivalent, and precipitation, which in turn impact the reservoir inflow. Therefore, it is imperative for us to quantify the impact of aerosols on reservoir inflow and to incorporate the aerosol models into future reservoir inflow forecasting models. In this paper, a comprehensive framework was developed to quantify the impact of aerosols on reservoir inflow by integrating the Weather Research and Forecasting model with Chemistry (WRF-Chem) and a dynamic regression model. The statistical dynamic regression model produces forecasts for reservoir inflow based on the meteorological output variables from the WRF-Chem model. The case study was performed on the Florence Lake and Lake Thomas Alva Edison of the Big Creek Hydroelectric Project in the San Joaquin Region. The simulation results show that the presence of aerosols results in a significant reduction of annual reservoir inflow by 4-14%. In the summer, aerosols reduce precipitation, snow water equivalent, and snowmelt that leads to a reduction in inflow by 11-26%. In the spring, aerosols increase temperature and snowmelt which leads to an increase in inflow by 0.6-2%. Aerosols significantly reduce the amount of inflow in the summer when the marginal value of water is extremely high and slightly increase the inflow in the spring when the run-off risk is high. In summary, the presence of aerosols is detrimental to the optimal utilization of hydroelectric power systems.

Many countries and regions across the globe broke their surface temperature records in recent years. Recent crippling heat waves swept across the Earth, further sparking concerns about the impending arrival of “tipping points” later in the 21st century. This study analyzes observed global surface temperature trends in three target latitudinal regions: the Arctic Circle, Tropics, and the Antarctic Circle. We show that global warming is accelerating unevenly across the planet, with the Arctic warming at more than three times the average rate of our world. We also analyzed the reliability of latitude-dependent surface temperature simulations from a suite of Coupled Model Intercomparison Project Phase 6 (CMIP6) models and their multi-model mean (MMM) by comparing their outputs to observational data sets. We selected the best-performing models based on their statistical abilities to reproduce historical, latitude-dependent values adapted from these data sets. The surface temperature projections were calculated from ensemble simulations of the Shared Socioeconomic Pathway 2–4.5 (SSP2–4.5) by the selected CMIP6 models. We estimate the calendar years of when surface temperatures will increase by 1.5, 2.0, and 2.5°C relative to the preindustrial period, both globally and in the three target regions. Our results reaffirm a dramatic, upward trend in projected surface temperatures, with unprecedented acceleration in the Arctic Circle, which could lead to catastrophic consequences across the Earth. Further studies are necessary to determine the most efficient solutions to reduce global warming acceleration and maintain a low SSP, both globally and regionally.

The interactions between aerosols and ice clouds represent one of the largest uncertainties in global radiative forcing from pre-industrial time to the present. In particular, the impact of aerosols on ice crystal effective radius (R _ei), which is a key parameter determining ice clouds' net radiative effect, is highly uncertain due to limited and conflicting observational evidence. Here we investigate the effects of aerosols on R _ei under different meteorological conditions using 9-year satellite observations. We find that the responses of R _ei to aerosol loadings are modulated by water vapor amount in conjunction with several other meteorological parameters. While there is a significant negative correlation between R _ei and aerosol loading in moist conditions, consistent with the "Twomey effect" for liquid clouds, a strong positive correlation between the two occurs in dry conditions. Simulations based on a cloud parcel model suggest that water vapor modulates the relative importance of different ice nucleation modes, leading to the opposite aerosol impacts between moist and dry conditions. When ice clouds are decomposed into those generated from deep convection and formed in situ, the water vapor modulation remains in effect for both ice cloud types, although the sensitivities of R _ei to aerosols differ noticeably between them due to distinct formation mechanisms. The water vapor modulation can largely explain the difference in the responses of R _ei to aerosol loadings in various seasons. A proper representation of the water vapor modulation is essential for an accurate estimate of aerosol-cloud radiative forcing produced by ice clouds.

Aerosol-cloud interactions (aerosol indirect effects) play an important role in regional meteorological variations, which could further induce feedback on regional air quality. While the impact of aerosol-cloud interactions on meteorology and climate has been extensively studied, their feedback on air quality remains unclear. Using a fully coupled meteorology-chemistry model, we find that increased aerosol loading due to anthropogenic activities in China substantially increases column cloud droplet number concentration and liquid water path (LWP), which further leads to a reduction in the downward shortwave radiation at surface, surface air temperature and planetary boundary layer (PBL) height. The shallower PBL and accelerated cloud chemistry due to larger LWP in turn enhance the concentrations of particulate matter with diameter less than 2.5 μm (PM_2.5) by up to 33.2 μg m⁻³ (25.1%) and 11.0 μg m⁻³ (12.5%) in January and July, respectively. Such a positive feedback amplifies the changes in PM_2.5 concentrations, indicating an additional air quality benefit under effective pollution control policies but a penalty for a region with a deterioration in PM_2.5 pollution. Additionally, we show that the cloud processing of aerosols, including wet scavenging and cloud chemistry, could also have substantial effects on PM_2.5 concentrations.

We investigate the air quality impact of record-breaking wildfires in Southern California during 5-18 December 2017 using the Weather Research and Forecasting model with Chemistry in combination with satellite and surface observations. This wildfire event was driven by dry and strong offshore Santa Ana winds, which played a critical role in fire formation and air pollutant transport. By utilizing fire emissions derived from the high-resolution (375 × 375 m²) Visible Infrared Imaging Radiometer Suite active fire detections, the simulated magnitude and temporal evolution of fine particulate matter (PM_2.5) concentrations agree reasonably well with surface observations (normalized mean bias = 4.0%). Meanwhile, the model could generally capture the spatial pattern of aerosol optical depth from satellite observations. Sensitivity tests reveal that using a high spatial resolution for fire emissions and a reasonable treatment of plume rise (a fair split between emissions injected at surface and those lifted to upper levels) is important for achieving decent PM_2.5 simulation results. Biases in PM_2.5 simulation are relatively large (about 50%) during the period with the strongest Santa Ana wind, due to a possible underestimation of burning area and uncertainty in wind field variation. The 2017 December fire event increases the 14-day averaged PM_2.5 concentrations by up to 231.2 μg/m³ over the downwind regions, which substantially exceeds the U.S. air quality standards, potentially leading to adverse health impacts. The human exposure to fire-induced PM_2.5 accounts for 14-42% of the annual total PM_2.5 exposure in areas impacted by the fire plumes.