Abstract:On the basis of the observed outgoing longwave radiation (OLR) data and other reanalysis datasets during 1979–2018, three categories of significant inter-monthly variations of the warm pool convection in the western Pacific are identified. The first category shows a negative OLR anomaly in June and August and a positive OLR anomaly in July. By contrast, the second category shows an opposite OLR anomaly to the first category. Meanwhile, the third category shows a positive OLR anomaly in June and July and a negative OLR anomaly in August. All categories of inter-monthly variations are related to the ENSO background. The first and second categories occur in relatively weak La Niña years and El Niño developing years, which are closely associated with sea surface temperature (SST) anomaly in spring over the warm pool. When the SST is high in the preceding month, convection in the succeeding month is enhanced along with a reduced SST. Consequently, when the SST is low in the preceding month, convection in the succeeding month is suppressed along with an enhanced SST. The local air–sea interaction in the warm pool plays a key role in the first and second categories. Different from the two other categories, the third category occurs in El Niño decaying years, which is related to a high SST in spring over the tropical Indian Ocean. During June and July, convection near India is enhanced because of the high SST in the tropical Indian Ocean. Through the excitation of a Kelvin wave propagating eastward, convection in the warm pool is suppressed. In the meantime, the enhanced convection near India reduces the local SST and suppresses convection in August when the influence from the Indian Ocean on the warm pool convection is considerably weakened. By contrast, the warm pool SST in August tends to increase because of suppressed convection in June and July. As a result, the warm pool convection is enhanced in August. Therefore, the third category results from the combined effects of tropical Indian Ocean forcing and local air–sea interaction in the warm pool.

Abstract:A regional air quality model system driven by the weather research and forecasting model is applied to investigate the distribution and evolution of aerosol components in Beijing during the springs of 2014. The synoptic conditions, meteorological variables, and characteristics of aerosol chemical components are comparatively analyzed. Moreover, the effects of heterogeneous reactions on dust and anthropogenic aerosol surface on chemical compositions during the dust (17 Mar and 29 Mar 2014) and haze (25–27 Mar 2014) periods are quantified and compared. The comparison with the observations indicates that the model is capable of reproducing the meteorological variables, PM2.5, and PM10, and their chemical component concentrations during the study period. Moreover, the inclusion of heterogeneous reactions apparently improves the prediction accuracy of PM2.5 and chemical component concentration. In dust days, dust is the dominant component of PM10 mass (50.7%), and its percentage contribution to PM2.5 is comparable to that of organic material (OM) and primary particulate matter (PPM). In hazy days, nitrate (25.6%) and OM (23.6%) contribute the most to PM2.5 mass. Meanwhile, the fractions of nitrate, PPM, and OM in PM10 are comparable. The fraction of coarse particle considerably increases during dusty days, with the mean fraction of 45.5% in PM10. In hazy days, fine particle dominates the PM10 mass, with a fraction of 85.6%. The heterogeneous reactions increase sulfate and nitrate concentrations by 16.9% and 83.8% in dusty days and by 14.5% and 45.0% in hazy days, respectively. On an average, the heterogeneous reactions lead to changes in near-surface SO₂, NO₂, O₃, sulfate, ammonium, and nitrate concentrations by -2.5%, -5.7%, -3.4%, 11.7%, 18.6%, and 58.5%, respectively, in Beijing during March 2014 thereby highlighting the important role of heterogeneous reactions in secondary aerosol formation.

Abstract:Super-high-resolution numerical simulations on the wind environment in neighborhoods have been a hot research area in urban meteorology. In this study, a computational fluid dynamics model based on large-eddy simulation was utilized to simulate the climatic wind environment in Longhu Community in Chongqing, and the impacts of local building configurations on the fine-scale structures of the wind field was investigated. Results show that complex underlying surfaces played an important role in regulating local circulations. The strong winds were mainly found over open spaces and at broad streets parallel to the background inflow. The overall wind speed in summer was larger than that in other seasons and could reach a magnitude of 0.8 m/s. Different building configurations led to different patterns of local wind fields. The isolated tall building resulted in strong downward motions and winding flows at the windward side of the building, where strong winds frequently occurred. The scattered low buildings had little impact on the local inflow, resulting in a wind field with a homogeneous pattern. The densely built tall complex with an enclosing shape greatly blocked the wind, which led to a relatively weak wind speed in the vicinity and was unfavorable for pollutant dispersion.

Abstract:The influence of gradual external forcing changes on non-stationary system is significant, and the manner by which external forcing features are reconstructed from non-stationary system has become the key to study the dynamic characteristics of the system. In this study, a continuous system (the modified Lorenz system) is used as the reference model, based on the slow feature analysis (SFA). We discuss the ability of SFA in extracting different forcing signals in the model under conditions of periodic forcing, weakened periodic forcing, exponential decay forcing, and periodic forcing with exponential decay. Results show that the SFA method can extract external forcing information acting on the continuous system and its extraction effect is correlated to the intensity of the external forcing, noise, and embedding dimension m: The weaker the external forcing or the stronger the noise interference, the worse the extraction effect. Hence, the false high-frequency fluctuation appears in the extracted signal. The increase in embedding dimension m can improve the extraction effect of the external forcing signal to a certain extent. The results also shows that the external forcing acting on a single variable embeds its driving information in the system and SFA can extract the external forcing signal from other variables.

Abstract:Nowadays, dynamical climate models are inefficient in meeting the real needs of climate prediction. An effective method is the combination of dynamical and statistical models. This combination integrates large-scale circulation information from the dynamical models into the statistical model to improve the prediction skill. On the basis of the higher prediction skill for the large-scale summer circulation variable of climate models and the significant relationship between the preceding ENSO signal and summer precipitation in China, a hybrid statistical downscaling prediction method for summer precipitation anomaly prediction in China was proposed in this paper. Cross validation of seasonal prediction for summer precipitation in China was performed, and results showed that the downscaling method improved the multi-year average of anomaly correlation coefficient significantly. In real application, the average PS score reached 71.5/72.7 during 2013–2018/2015–2018, which is higher than that of the original model and the operational predictions issued by the Beijing Climate Center. This statistical downscaling model, which has stable predictive skill, is one of the effective references for operational seasonal prediction in China.

Abstract:Temperate grasslands are important components of terrestrial ecosystems. Investigating the grassland carbon exchange processes and their impact factors is essential to assess the variations in the carbon source–sink of terrestrial ecosystems and their responses to future climate change. On the basis of the eddy covariance measurements of carbon fluxes of meadow steppe at Tongyu during 2011–2017 and typical steppe at Maodeng during 2013–2017, the diurnal variation of carbon fluxes and its responses to environmental factors were analyzed. Results showed that both grasslands had the strongest carbon uptake in July. The monthly peaks of gross primary production (GPP), ecosystem respiration (R_e), and net ecosystem exchange (NEE) of meadow steppe were greater than those of typical steppe. The diurnal variation of NEE was dominated by a unimodal pattern. However, when the saturated vapor pressure difference was high in July and August, GPP decreased around noon, leading to a bimodal pattern of NEE. Photosynthetically active radiation was the key factor in the diurnal variation of NEE of meadow steppe. Meanwhile, the diurnal variation of NEE of typical steppe was susceptible to shallow soil water content (5 cm). Water deficit led to a significant decrease in NEE at both grasslands. However, the meadow steppe carbon sequestration rate was more sensitive to water deficit than the typical steppe carbon sequestration rate. Meanwhile, water deficit modified the responses of GPP, R_e, and NEE to temperature and photosynthetically active radiation.

Abstract:Using the WRF/Chem (Weather Research Forecasting/Chemistry) model, a large-scale PM2.5 heavy pollution process in northern China from 25 November to 2 December 2015 was simulated. Comparisons to observations show that the model can realistically capture the magnitude and variation of PM2.5 and meteorological factors, and can be used for the mechanism analysis of this pollution event. This paper further analyzed the mechanism of the strong pollution event from the aspects of dynamics, thermo-meteorological conditions, and chemical transformation. The results show that the dynamic factors mainly affect the pollution event through weakening of the surface wind and vertical wind shear. Thermal factors, such as a boundary layer inversion, promote the enhancement of the atmospheric stability, which is not conducive to pollutant diffusion. Based on the analysis of the PM2.5 composition, the nitrate, sulfate, and organic carbon content increased in this event, indicating that the secondary aerosol formation caused by vehicle exhaust and coal combustion contributes greatly to the PM2.5 pollution. To identify the main factors causing this pollution event, we used multiple linear regression and relative contribution rate accounting methods to quantify the multi-factor analysis. The results show that the thermal factors play a major role in the pollution process, with a variance contribution of 52%, dynamic factor of 34%, and a chemical transformation variance contribution of 14%, indicating that adverse meteorological conditions, especially thermal conditions, are the main causes of the pollution event.

Abstract:In this study, a heavy fog that occurred around the Bohai Sea on 17 December 2016, was investigated based on measurement data from microwave radiometer, wind profiler radar, four-component radiometer, sonic anemometer-thermometer, satellite images, buoys, conventional surface observation, and FNL (Final) reanalysis. The synoptic system for fog formation and vertical characteristics of radiation and turbulence were studied as key analysis points. The results show the following: (1) The fog appeared in the front of a low-pressure area and the back of a high-pressure area, and the warm-wet advection accompanied by strong low-level jet provided stable inversion and continuous water vapor accumulation in the fog area, which was very helpful to the fog formation. (2) The water vapor flux was closely related to the movement of the low-level jet. The growth rate of near-surface specific humidity was proportional to the intensity of the low-level jet. (3) Water vapor transport humidified the lower-boundary layer atmosphere around the Bohai Sea, which enhanced the attenuation effect of atmospheric radiation, leading to a decrease in short-wave radiation and an increase in long-wave radiation. When the fog formed, the net radiation was approaching zero. (4) Inversion effectively inhibited the development of turbulence. The kinetic energy and friction velocity of the turbulence were weak in the near-surface layer.

Abstract:NCEP-FNL datasets are used as the initial and boundary fields of the WRF model. Six planetary boundary layer parameterization schemes (PBLPS) are applied in the model for Xinjiang region with 10-km horizontal resolution. The spatial distribution and temporal evolution of the meteorological elements are analyzed. The analysis results show the following aspects: 1) The WRF model with six PBLPS can simulate the seasonal circulation of monthly precipitation and the spatial pattern of annual and rainy season precipitation. 2) For the Xinjiang region, the deviation of rainy season precipitation between the simulation conducted with the Grenier-Bretherton-McCaa (GBM) scheme and the observations is within ±30%. For the Tianshan area, the deviation of annual precipitation between the simulation conducted with the Bougeault-Lacarrere (BouLac) scheme and the observations is －19.13%. The TS scores of moderate and heavy rains are 0.37 and 0.33, respectively, in the test results of daily precipitation simulated with the GBM scheme. For the different types of underlying surfaces in the Tianshan area, the day and night precipitation can be well simulated by the model with the GBM scheme with the deviation of precipitation within 5 mm during long precipitation days. 3) The WRF model with the BouLac scheme can simulate the annual spatial and temporal distribution characteristics of annual precipitation in the Tianshan area, and the rainy seasonal precipitation can be well simulated by the model with the GBM scheme in Xinjiang. Therefore, PLBS with the WRF model in the Xinjiang region should be considered.