No abstract

Abstract. Accompanied by unfavorable meteorological conditions with stable stratification in various haze regions of China, persistent heavy aerosol pollution episodes (HPEs) lasting more than 3 consecutive days frequently occur, particularly in winter. In the North China Plain (NCP), explosive growth of fine particulate matter smaller than 2.5 µm in diameter (PM2.5), which occurs during some HPES, is dominated by a two-way feedback mechanism between more unfavorable meteorological conditions and cumulative aerosol pollution. However, the existence of a two-way feedback mechanism such as this in other key haze regions in China is uncertain; these regions include the Guanzhong Plain (GZP), the Yangtze River Delta (YRD) region, the Two Lakes Basin (TLB; a large outflow basin connected to Hubei Province and Hunan Province), the Pearl River Delta (PRD) region, the Sichuan Basin (SB), and the Northeast China Plain (NeCP). In this study, using surface PM2.5 and radiation observations, radiosonde observations, and reanalysis data, we observed the existence of a two-way feedback mechanism in the six abovementioned regions. In the SB, this two-way feedback mechanism is weak due to the suppression of cloudy mid-upper layers. In the more polluted NCP, the GZP, and the NeCP, the feedback is more striking than that in the YRD, the TLB, and the PRD. In these regions, the feedback of worsened meteorological conditions on PM2.5 explains 60 %–70 % of the increase in PM2.5 during the cumulative stages (CSs). For each region, the low-level cooling bias becomes increasingly substantial with increasing aerosol pollution and a closer distance to the ground surface. With PM2.5 mass concentrations greater than 400 µg m−3, the near-ground bias exceeded −4 ∘C in Beijing and reached up to approximately −4 ∘C in Xi'an; this result was caused by accumulated aerosol mass to some extent. In addition to the increase in PM2.5 caused by the two-way feedback, these regions also suffer from the regional transport of pollutants, including inter-regional transport in the GZP, trans-regional transport from the NCP to the YRD and the TLB, and southwesterly transport in the NeCP.

The Beijing government implemented a number of clean air action plans to improve air quality in the last 10 years, which contributed to changes in the concentration of fine particles and their compositions. However, quantifying the impacts of these interventions is challenging as meteorology masks the real changes in observed concentrations. Here, we applied a machine learning technique to decouple the effect of meteorology and evaluate the changes in the chemistry of nonrefractory PM 1 (particulate matter less than 1 μm) in winter 2007, 2016, and 2017 as a result of the clean air actions. The observed mass concentrations of PM 1 were 74.6, 90.2, and 36.1 μg m −3 in the three winters, while the deweathered concentrations were 74.2, 78.7, and 46.3 μg m −3 , respectively. The deweathered concentrations of PM 1 , organics, sulfate, ammonium, chloride, SO 2 , NO 2 , and CO decreased by −38, −46, −59, −24, −51, −89, −16, and −52% in 2017 in comparison to 2007. On the contrary, the deweathered concentration of nitrates increased by 4%. Our results indicate that the clean air actions implemented in 2017 were highly effective in reducing ambient concentrations of SO 2 , CO, and PM 1 organics, sulfate, ammonium, and chloride, but the control of nitrate and PM 1 organics remains a major challenge.

No abstract

The two-dimensional nonhydrostatic compressible dynamical core for the atmosphere has been developed by using a new nodal-type high-order conservative method, the so-called multimoment constrained finitevolume (MCV) method. Different from the conventional finite-volume method, the predicted variables (unknowns) in an MCV scheme are the values at the solution points distributed within each mesh cell. The time evolution equations to update the unknown point values are derived from a set of constraint conditions based on the multimoment concept, where the constraint on the volume-integrated average (VIA) for each mesh cell is cast into a flux form and thus guarantees rigorously the numerical conservation. Two important features make the MCV method particularly attractive as an accurate and practical numerical framework for atmospheric and oceanic modeling. 1) The predicted variables are the nodal values at the solution points that can be flexibly located within a mesh cell (equidistant solution points are used in the present model). It is computationally efficient and provides great convenience in dealing with complex geometry and source terms. 2) High-order and physically consistent formulations can be built by choosing proper constraints in view of not only numerical accuracy and efficiency but also underlying physics. In this paper the authors present a dynamical core that uses the third-and the fourth-order MCV schemes. They have verified the numerical outputs of both schemes by widely used standard benchmark tests and obtained competitive results. The present numerical core provides a promising and practical framework for further development of nonhydrostatic compressible atmospheric models.

An explicit prognostic cloud‐cover scheme (PROGCS) is implemented into the Global/Regional Assimilation and Prediction System (GRAPES) for global middle‐range numerical weather predication system (GRAPES_GFS) to improve the model performance in simulating cloud cover and radiation. Unlike the previous diagnostic cloud‐cover scheme (DIAGCS), PROGCS considers the formation and dissipation of cloud cover by physically connecting it to the cumulus convection and large‐scale stratiform condensation processes. Our simulation results show that clouds in mid‐high latitudes arise mainly from large‐scale stratiform condensation processes, while cumulus convection and large‐scale condensation processes jointly determine cloud cover in low latitudes. Compared with DIAGCS, PROGCS captures more consistent vertical distributions of cloud cover with the observations from Atmospheric Radiation Measurements (ARM) program at the Southern Great Plains (SGP) site and simulates more realistic diurnal cycle of marine stratocumulus with the ERA‐Interim reanalysis data. The low, high, and total cloud covers that are determined via PROGCS appear to be more realistic than those simulated via DIAGCS when both are compared with satellite retrievals though the former maintains slight negative biases. In addition, the simulations of outgoing longwave radiation (OLR) at the top of the atmosphere (TOA) from PROGCS runs have been considerably improved as well, resulting in less biases in radiative heating rates at heights below 850 hPa and above 400 hPa of GRAPES_GFS. Our results indicate that a prognostic method of cloud‐cover calculation has significant advantage over the conventional diagnostic one, and it should be adopted in both weather and climate simulation and forecast.

No abstract

No abstract