Planetary boundary-layer (PBL) structure was investigated using observations from a Doppler lidar and the 325-m Institute of Atmospheric Physics (IAP) meteorological tower in the centre of Beijing during the summer 2015 Study of Urban-impacts on Rainfall and Fog/haze (SURF-2015) field campaign. Using six fair-weather days of lidar and tower data under clear to cloudy skies, we evaluate the ability of the Doppler lidar to probe the urban boundary-layer structure, and then propose a composite method for estimating the diurnal cycle of the PBL depth using the Doppler lidar. For the convective boundary layer (CBL), a threshold method using vertical velocity variance (σ 2 w > 0.1 m 2 s −2 ) is used, since it provides more reliable CBL depths than a conventional maximum wind-shear method. The nocturnal boundary-layer (NBL) depth is defined as the height at which σ 2 w decreases to 10 % of its near-surface maximum minus a background variance. The PBL depths determined by combining these methods have average values ranging from ≈270 to ≈1500 m for the six days, with the greatest maximum depths associated with clear skies. Release of stored and anthropogenic heat contributes to the maintenance of turbulence until late evening, keeping the NBL near-neutral and deeper at night than would be expected over a natural surface. The NBL typically becomes more shallow with time, but grows in the presence of low-level nocturnal jets. While current results are promising, data over a broader range of conditions are needed to fully develop our PBL-depth algorithms.
Abstract. Realistic simulation of the Earth's mean-state climate remains a major challenge, and yet it is crucial for predicting the climate system in transition. Deficiencies in models' process representations, propagation of errors from one process to another, and associated compensating errors can often confound the interpretation and improvement of model simulations. These errors and biases can also lead to unrealistic climate projections and incorrect attribution of the physical mechanisms governing past and future climate change. Here we show that a significantly improved global atmospheric simulation can be achieved by focusing on the realism of process assumptions in cloud calibration and subgrid effects using the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The calibration of clouds and subgrid effects informed by our understanding of physical mechanisms leads to significant improvements in clouds and precipitation climatology, reducing common and long-standing biases across cloud regimes in the model. The improved cloud fidelity in turn reduces biases in other aspects of the system. Furthermore, even though the recalibration does not change the global mean aerosol and total anthropogenic effective radiative forcings (ERFs), the sensitivity of clouds, precipitation, and surface temperature to aerosol perturbations is significantly reduced. This suggests that it is possible to achieve improvements to the historical evolution of surface temperature over EAMv1 and that precise knowledge of global mean ERFs is not enough to constrain historical or future climate change. Cloud feedbacks are also significantly reduced in the recalibrated model, suggesting that there would be a lower climate sensitivity when it is run as part of the fully coupled E3SM. This study also compares results from incremental changes to cloud microphysics, turbulent mixing, deep convection, and subgrid effects to understand how assumptions in the representation of these processes affect different aspects of the simulated atmosphere as well as its response to forcings. We conclude that the spectral composition and geographical distribution of the ERFs and cloud feedback, as well as the fidelity of the simulated base climate state, are important for constraining the climate in the past and future.
Abstract. We investigated the interactions between the air pollutants and the structure of the urban boundary layer (UBL) over Beijing by using the data mainly obtained from the 325 m meteorological tower and a Doppler wind lidar during 1–4 December 2016. Results showed that the pollution episodes in this period could be characterized by low surface pressure, high relative humidity, weak wind, and temperature inversion. Compared with a clean daytime episode that took place on 1 December, results also showed that the attenuation ratio of downward shortwave radiation was about 5 %, 24 % and 63 % in afternoon hours (from 12:00 to 14:00 local standard time, LST) on 2–4 December, respectively, while for the net radiation (Rn) attenuation ratio at the 140 m level of the 325 m tower was 3 %, 27 % and 68 %. The large reduction in Rn on 4 December was not only the result of the aerosols, but also clouds. Based on analysis of the surface energy balance at the 140 m level, we found that the sensible heat flux was remarkably diminished during daytime on polluted days and even negative after sunrise (about 07:20 LST) till 14:00 LST on 4 December. We also found that heat storage in the urban surface layer played an important role in the exchange of the sensible heat flux. Owing to the advantages of the wind lidar having superior spatial and temporal resolution, the vertical velocity variance could capture the evolution of the UBL well. It clearly showed that vertical mixing was negatively related to the concentrating of pollutants, and that vertical mixing would also be weakened by a certain quantity of pollutants, and then in turn worsened the pollution further. Compared to the clean daytime on 1 December, the maximums of the boundary layer height (BLH) decreased about 44 % and 56 % on 2–3 December, when the average PM2.5 (PM1) concentrations in afternoon hours (from 12:00 to 14:00 LST) were 44 (48) µg m−3 and 150 (120) µg m−3. Part of these reductions of the BLH was also contributed by the effect of the heat storage in the urban canopy.
The urban heat island (UHI) effect might cause extreme weather, which would seriously affect people's health, increase energy consumption and cause other negative impacts. To construct urban green spaces is a feasible strategy to effectively weaken the UHI effect. In this study, the cooling effect of green spaces on the UHI effect was carefully investigated in summer and winter in Harbin city. Specifically, the vegetation index and surface temperature information were extracted by the grid method, and based on this data, the relationship between the urban green space and the UHI effect was analyzed quantitatively. In summer, the cooling effect is more significant. The average cooling extent reached 1.65 • C, the average maximum temperature change was 7.5 • C, and the cooling range was mainly 120 m. The cooling effect can be improved by adjusting the green space area, perimeter and shape. Increasing the green area (within 37 ha) or the green circumference (within 5300 m) can most economically improve its cooling effect. The shape factor would significantly affect the cooling effect within 0.03. The simpler the green space shape, the more obvious the cooling effect. In contrast, in winter the green spaces had a certain cooling effect when there was no snow cover or little snow cover, although this was still less significant compared with the situation in summer. The average cooling extent reached 0.48 • C, the average maximum temperature change was 4.25 • C, and the cooling range was mainly 90 m. However, there is no correlation between urban green space and the UHI effect in areas mainly covered by ice and snow. This work could provide protocols for urban green space design to effectively control the UHI effect of sub-frigid cities.
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