2020, 25(4):345-352. DOI: 10.3878/j.issn.1006-9585.2019.19046
Abstract:Using the National Center for Atmospheric Research Community Atmosphere Model version 3 (CAM3) outputs and European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim) data, the role of the orography of the Tibetan and Iranian Plateaus in modulating the sources of stationary wave energy is investigated in this study. The sources of stationary wave energy in the troposphere during winter are located in two areas, i.e., East Asia north of the plateau and western Pacific downstream of the plateau. When orographic uplift occurs, the baroclinic development weakens over East Asia north of the plateau and enhances over western Pacific downstream of the plateau in the troposphere. The location of the barotropic development of stationary wave energy is similar to that of baroclinic development. Meanwhile, the intensity of the barotropic development of stationary wave energy is weaker than that of baroclinic development in the troposphere. When orographic height uplift occurs, the barotropic development of stationary wave energy first weakens and then enhances over East Asia north of the plateau, whereas it enhances over Western Pacific downstream of the plateau in the troposphere. In the troposphere during winter, the total stationary wave energy development is consistent with the baroclinic development of stationary wave energy, which indicates that the baroclinic development of stationary wave energy plays an important role in the development of stationary wave.
2020, 25(4):353-365. DOI: 10.3878/j.issn.1006-9585.2019.19052
Abstract:The MJO (Madden-Julian Oscillation) simulation ability of the numerical experiments from CNRM GCMs (General Circulation Models) participated in the MJOTF/GASS (MJO Task Force/Global Energy and Water Cycle Experiment Atmospheric System Study) project was evaluated by tracking the eastward propagating positive equatorial precipitation anomalies. The GCMs included a fully coupled simulation (CNRM-CM), a half-coupled simulation (CNRM-ACM), and an uncoupled simulation (CNRM-AM) based on 1991-2010 data. The possible impacts of air-sea coupling on the MJO simulation were investigated. The CNRM-CM showed the highest ability in simulating the MJO characteristics, in terms of the occurrence frequency, amplitude, and propagation range. The climatological sea surface temperature (SST) of the CNRM-CM and CNRM-ACM showed a distinct cold bias over the Indo-Pacific warm pool region, compared with that of the CNRM-AM. The cold bias did not strongly impact the MJO simulation ability compared with the impacts on the MJO simulation abilities of the CNRM-ACM and CNRM-AM. The zonal gradient of the intraseasonal SST was significant in the CNRM-CM, with a strong positive intraseasonal SST anomaly to the east of the MJO convection center and a negative intraseasonal SST anomaly to the west of the MJO convection center when the MJO was over the Indian Ocean. In contrast, such a gradient was lost in the CNRM-ACM and CNRM-AM. The results indicate that the impact of the air-sea coupling on the MJO simulations by the CNRM GCMs was mainly through the influences on the intraseasonal SST variability.
2020, 25(4):366-384. DOI: 10.3878/j.issn.1006-9585.2020.20005
Abstract:Eight latest scenarios (SSPx-y scenarios), which are based on different shared socioeconomic paths (SSPs) are adopted in the Coupled Model Intercomparison Project Phase 6 (CMIP6) to project the probable magnitude and trend of future climate changes. In this article, the emission datasets of various major greenhouse gases and aerosols under the eight SSPx-y scenarios are analyzed, including emission intensities in the reference year (i.e., 2015), spatial and temporal variations of future emission intensities, and yearly change in emission intensities for the six typical selected sub-regions. Results show that the strongest emission intensities of carbon dioxide (CO2), methane (CH4), black carbon (BC), and sulfur dioxide (SO2) are distributed mainly in East and South Asia in 2015. In comparison with the reference year, variations in the intensities of CO2 and CH4 emissions in
2020, 25(4):385-398. DOI: 10.3878/j.issn.1006-9585.2019.19040
Abstract:Two types of mesoscale convective systems (MCSs) generated over the eastern Tibetan Plateau (TP) during 16 consecutive warm seasons were identified and tracked by an automatic tracking algorithm based on hourly geostationary satellite TBB data that were provided by Kochi University. Following the manual verification of the automatic tracking results, statistical and comparative analyses of these two types of MCSs were conducted using NOAA’s CMORPH (Climate Prediction Center Morphing) precipitation data and NCEP’s CFSR (Climate Forecast System Reanalysis) reanalysis data. The main results show that July and August were the most active months regarding the MCSs’ generation over the eastern section of the plateau, but the percentages of MCSs’ vacating the TP of these two months were the lowest. In May, the number of MCSs generated reached a minimum, but up to nearly 40% of the MCSs could vacate the TP. The MCSs that could vacate the TP (V-MCS) usually showed a longer lifespan, earlier triggering time, and lower proportion of short lifespan cases, compared with the MCSs that could not vacate the TP (N-MCS). During the period of the research, the V-MCSs were usually faster in development and stronger in intensity, compared with the N-MCSs. However, owing to the much lower frequency in the occurrence of V-MCSs, their contribution to the local precipitation was only about 15%, which was approximately half the contribution of the N-MCSs. The composite circulation features of the V-MCSs and N-MCSs that were generated over the eastern plateau were significantly different. The shortwave trough and stronger westerly wind in the middle troposphere and the cyclonic wind shear in the lower troposphere provided more favorable conditions for the V-MCSs’ occurrence, maintenance, and eastward displacement. In contrast, divergence conditions in the upper troposphere were more conducive to the N-MCSs (the associated South Asia high in this type was stronger).
2020, 25(4):399-409. DOI: 10.3878/j.issn.1006-9585.2019.19050
Abstract:Wetlands play important roles regulating the local microclimate. Studying the characteristics of wetland microclimate effects can help to specifically understand the impact of the wetlands on the local microclimate. In this study, we chose Hengshui Lake in Hengshui City, Hebei Province, as the study area and analyzed the microclimate effects of Hengshui Lake during different seasons by comparing the meteorological elements inside and outside the lake. The data came from 11 conventional meteorological stations in Hengshui City. The results show that: (1) Hengshui Lake has a remarkable cold island effect, wet island effect, and a wind island effect, which modify the surrounding climate characteristics. (2) The microclimate effect of Hengshui Lake has an obvious seasonal characteristic. The order of the average cold island effect during the four seasons is spring > winter > autumn > summer; the order of the wet island effect is summer > spring > autumn > winter; and the order of the wind island effect is spring > summer > winter > autumn. The microclimate effect is intense in spring. (3) Hengshui Lake shows an obvious circadian rhythm in its microclimate effect. The cold island effect is stronger at night than during the day, while the wet island and wind island effects are stronger during the day than at night.
2020, 25(4):410-418. DOI: 10.3878/j.issn.1006-9585.2019.19038
Abstract:Long-term snow-depth observations at Arxan station are used to evaluate the application of snow-depth products retrieved from the AMSR-E and AMSR-2 microwave data and Chinese snow-depth dataset developed by Chinese researchers and to establish a new snow-depth retrieval algorithm. The statistical snow-cover days and maximum snow-depth records derived from the 35-year Chinese snow-depth dataset and station observations are consistent, particularly after 2000. The snow-depth variation trend estimated from the snow-depth products retrieved from the AMSR-E and AMSR-2 microwave data is consistent with that retrieved from the station observations, with the correlation coefficient greater than 0.6. However, the variation range of the snow-depth products is wider than that of the station observations. Thus, the root mean square error (RMSE) of both snow-depth datasets is high (i.e., approximately 13 cm). The Chinese snow-depth dataset at Arxan station shows a higher correlation coefficient of 0.65 and a lower RMSE of 6.3 cm than the station observations. To better estimate snow depth in the Arxan region, a new snow-depth retrieval method is developed using both space-borne passive microwave brightness temperature and observed snow-depth data at Arxan station. The validation shows that the snow-depth data retrieved using the new method has a higher correlation with the observations (i.e., approximately 0.77) and a lower RMSE (i.e., approximately 4.68 cm) than the snow-depth products retrieved from the AMSR-E and AMSR-2 microwave data and Chinese snow-depth dataset used in this study.
2020, 25(4):419-428. DOI: 10.3878/j.issn.1006-9585.2020.19061
Abstract:To apply Holroyd’s proposed cloud particle habit classification method to the ice particle measured by airborne cloud imaging probe (CIP) in northern China, this article improves the thresholds in the Holroyd’s cloud particle habit classification based on historical airborne measurement data, which makes the habit classification method more suitable for the ice particle measured by CIP in northern China. The improved threshold method is used to process and analyze the airborne measurement data from a precipitation stratus cloud in Shanxi Province. It is found that there are four types of ice particles with a frequency of occurrence greater than 15% in the vertical and horizontal distribution in this stratiform precipitation cloud; three among them are relatively fixed, namely, graupel, line shape, and irregular type. The fourth type is related to the specific cloud environment. In the vertical direction, it is dendrite (-8 to 0℃) or tiny (-12 to 8℃), and in the horizontal direction of different heights it is dendrite (
2020, 25(4):429-442. DOI: 10.3878/j.issn.1006-9585.2020.20011
Abstract:The variability and corresponding mechanisms of the Arctic Oscillation (AO) during three typical periods, the Medieval Climate Anomaly (MCA), Little Ice Age (LIA), and Present Warm Period (PWP), in the last millennium were analyzed using simulations from nine Earth system models (ESMs) from the Paleoclimate Modeling Intercomparison Project Phase III (PMIP3) Last Millennium experiment and Paleoclimate Model Intercomparison Project Phase 5 (CMIP5) historical experiments. Compared with the NCEP reanalysis data, the ESMs reasonably reproduce the AO spatial pattern and inter-annual period, and most ESMs reproduce the AO strengthening trend in the last five decades. Simulations show that there is no consistent AO phase during the MCA among the different models. The eight models simulated generally negative AO phases during the LIA and positive phases during the PWP. These simulated results are consistent with previous studies using proxy reconstructions and observations. The multi-model ensemble mean indicates that there is no significant sea level pressure (SLP) change over the Arctic region during the MCA. The SLP anomalies over the Arctic region are significantly positive during the LIA and significantly negative during the PWP. These changes in SLP are related to the anomalous lower temperature during the LIA and higher temperature during the PWP over the Arctic region. Our study suggests that the AO variability during the LIA and PWP are influenced by the natural and anthropocentric forcing, respectively.
2020, 25(4):443-456. DOI: 10.3878/j.issn.1006-9585.2020.20013
Abstract:The default land use data used in the Weather Research and Forecasting Model (WRF) differ significantly from the actual land use situation, which affects the simulation results. For this reason, many researchers have proposed schemes for updating land use data prior to running the model. The simplest method involved correcting the size of the urban area. Due to the heterogeneity of the urban surface, it has been suggested that the urban landscape be subdivided into refined classification areas. However, in the literature on the impact of land use data on the WRF model, most studies have only compared the simulation results before and after data updating, and have not distinguished the two factors of changes in the urban areas and urban heterogeneity. In this paper, the authors consider the use of urban area correction and refined classification synthetically. In the area correction scheme and the two refined classification schemes, three kinds of optimized land use data are generated. Combined with default land use data, the authors established four cases to simulate two high temperature weather processes that occurred in August 2018 and August 2019 in Shanghai. The results of these two simulations are: 1) The simulation results for temperature, relative humidity, and wind speed were improved after the land use data in the WRF model had been updated. 2) The size of an urban area is the most critical factor affecting the temperature. The area correction reduces reduced the average root mean square error (RMSE) by 0.86℃, but the refined classification reduced the average RMSE by just 0.04℃ at most. 3) The refined classification method primarily affected the wind speed and relative humidity. Although area correction reduced the average RMSE of wind speed by just 0.04 m/s, the refined classification method further reduced the RMSE by up to 0.19 m/s. The mean RMSE of relative humidity was reduced by just 0.23% by area correction, while the maximum RMSE was reduced by 2.25% by refined classification. 4) Generally speaking, to some extent, the heterogeneity of a city is considered in refined classification schemes, so the simulation results for temperature, relative humidity, and wind speed are improved to a greater extent, and the more detailed is the classification, the better is the effect.