Dust storms have large impacts on air quality and meteorological elements; however, their relationships with atmospheric greenhouse gases (e.g., CO₂) and radiation components remain uncertain. In this study, the co-variation of dust and CO₂ concentrations and its possible influencing mechanism are examined using observations at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station along with simulations of the Vegetation Photosynthesis and Respiration Model coupled with the Weather Research and Forecasting model (WRF-VPRM), during two dust storm events on March 15 and 28, 2021. During these events, hourly CO₂ concentrations decreased by 40–50 ppm at SDZ while dust concentrations increased to 1240.6 and 712.4 µg m⁻³. The elevated dust increased diffusive shortwave irradiance by 50–60% and decreased direct shortwave irradiance by ~60% along with clouds. The dust events were attributed to the passages of two cold front systems over northern China. At SDZ, during the frontal passages, wind speed increased by 3–6 m s⁻¹ and relative humidity decreased by 50–60%. The CO₂ variations associated with the frontal systems were captured by the WRF-VPRM despite the overestimated surface CO₂ level at SDZ. Biogenic CO₂ flux plays an indistinctive role on the large CO₂ variation at SDZ as it is weak during the non-growing season. The cold fronts pushed polluted air southeastward over the North China Plain and replaced it with low-CO₂ air from Northwest China, leading to the declines in CO₂. These findings demonstrate that meso-scale synoptic conditions significantly affect the regional transport and dispersion of CO₂, which can influence the prediction of terrestrial carbon balance on a regional scale.

Planetary boundary layer (PBL) schemes parameterize unresolved turbulent mixing within the PBL and free troposphere (FT). Previous studies reported that precipitation simulation over the Amazon in South America is quite sensitive to PBL schemes and the exact relationship between the turbulent mixing and precipitation processes is, however, not disentangled. In this study, regional climate simulations over the Amazon in January-February 2019 are examined at process level to understand the precipitation sensitivity to PBL scheme. The focus is on two PBL schemes, the Yonsei University (YSU) scheme, and the asymmetric convective model v2 (ACM2) scheme, which show the largest difference in the simulated precipitation. During daytime, while the FT clouds simulated by YSU dissipate, clouds simulated by ACM2 maintain because of enhanced moisture supply due to the enhanced vertical moisture relay transport process: 1) vertical mixing within PBL transports surface moisture to the PBL top, and 2) FT mixing feeds the moisture into the FT cloud deck. Due to the thick cloud deck over Amazon simulated by ACM2, surface radiative heating is reduced and consequently the convective available potential energy (CAPE) is reduced. As a result, precipitation is weaker from ACM2. Two key parameters dictating the vertical mixing are identified, p, an exponent determining boundary layer mixing and lambda, a scale dictating FT mixing. Sensitivity simulations with altered p, lambda, and other treatments within YSU and ACM2 confirm the precipitation sensitivity. The FT mixing in the presence of clouds appears most critical to explain the sensitivity between YSU and ACM2.

Terrestrial carbon fluxes are crucial to the global carbon cycle. Quantification of terrestrial carbon fluxes over the Tibetan Plateau (TP) has considerable uncertainties due to the unique ecosystem and climate and scarce flux observations. This study evaluated our recent improvement of terrestrial flux parameterization in the weather research and forecasting model coupled with the vegetation photosynthesis and respiration model (WRF-VPRM) in terms of reproducing observed net ecosystem exchange (NEE), gross ecosystem exchange (GEE), and ecosystem respiration (ER) over the TP. The improvement of VPRM relative to the officially released version considers the impact of water stress on terrestrial fluxes, making it superior to the officially released model due to its reductions in bias, root mean square error (RMSE), and ratio of standard deviation (RSD) of NEE to 0.850 µmol·m⁻² ·s ⁻¹, 0.315 µmol·m⁻² ·s ⁻¹, and 0.001, respectively. The improved VPRM also affects GEE simulation, increasing its RSD to 0.467 and decreasing its bias and RMSE by 1.175 and 0.324 µmol·m⁻² ·s ⁻¹, respectively. Furthermore, bias and RMSE for ER were lowered to −0.417 and 0.954 µmol·m⁻² ·s ⁻¹, with a corresponding increase in RSD by 0.6. The improved WRF-VPRM simulation indicates that eastward winds drive the transfer of lower CO2 concentrations from the eastern to the central and western TP and the influx of low-concentration CO2 inhibits biospheric CO2 uptake. The use of an improved WRF-VPRM in this study helps to reduce errors, improve our understanding of the role of carbon flux cycle over the TP, and ultimately reduce uncertainty in the carbon flux budget.

Severe air pollution plagues Arequipa Peru due to anthropogenic and natural emissions. Persistent volcano outgassing in the vicinity lead Arequipa ranked among the largest SO₂ sources in the world. Since the volcano plumes mostly exist in the free troposphere and stratosphere where horizontal transport acts rather quickly, previous studies mostly focused on global scale impact of volcano plumes. Whether these plumes can be transported to near the surface and affect ambient air quality barely gets research attention. For the first time this study using WRF-Chem simulations to reveal that in the presence of favorable meteorological conditions, the plume from volcano Sabancaya can be transported to Arequipa through a series of advection and dispersion processes: 1), in presence of northerly/northwesterly winds the free troposphere plume from Sabancaya is captured by mountain Chachani and transported downward to Arequipa by nighttime downstream gravity wave. The extent of downward penetration of plume depends on the free troposphere winds and stability. Often the plume is downward transported to above the boundary layer over Arequipa during nighttime. 2), on the following day, convective boundary layer growth further transports the plume above the boundary layer to near the surface through vertical mixing processes, thus exacerbating ambient air pollution over Arequipa. This modeling study discovers a mechanism of how the volcano plumes exacerbate air pollution over Arequipa using SO₂ as a tracer. The quantitative contribution of volcano plumes to ambient aerosol pollution needs to be examined using model simulations including aerosol processes and more accurate volcano emissions.