Abstract:The effects of anthropogenic emission inventories on simulated air pollutants concentrations in Zheijiang Province have been analyzed using the WRF-Chem (Weather Research Forecast-Chemistry) model. Three independent emission inventories, i.e. INTEX-B (Intercontinental Chemical Transport Experiment-Phase B), REASv2.1 (Regional Emission Inventory in Asia version 2.1), and HTAP_v2 (Hemispheric Transport of Air Pollution version 2), are used for model simulations during December 2013. The three experiments are denoted by IN, RE, and HT, respectively. Compared with in situ measurements, the three experiments can reasonably reproduce the temporal and spatial characteristics of PM2.5, PM10, and NO₂ surface concentrations with correlation coefficients ranging from 0.5 to 0.8. More than 85% of simulated values are within the range of 0.5 to 2 times of observational values. However, all of them have a poor performance on simulation of SO₂ concentration. The relative biases of PM2.5 and PM10 concentrations simulated by IN, RE, and HT are about 30%, 16%, and 6%, respectively, and the best performance is obtained by HT. The PM2.5 primary emissions and the secondary aerosol precursor SO₂ emissions of INTEX-B are significantly higher than those of REASv2.1 and HTAP_v2 emission inventories, which results in more sulfate aerosols and subsequently increases the PM2.5 concentration. The obviously lower NH₃ emission of HTAP_v2 compared to that in the other two emission inventories inhibits the formation of nitrate aerosols, which helps to reduce the PM2.5 concentration. Differences between the base year of emission inventories and the simulation year have greater impacts on the accuracy of simulated SO₂ concentrations than that of PM2.5, PM10, and NO_x. SO₂ emissions of INTEX-B are significantly higher than those of REASv2.1 and HTAP_v2 emission inventories, especially for the northern part of Zhejiang Province and the coastal industrialized areas, which is the primary reason for the obvious overestimation of SO₂ using the INTEX-B inventory. NO_x emissions of the three emission inventories are very consistent over Zhejiang Province, which could be the main reason for the similar modeled values and small relative biases (-8%-4%) of NO₂ in the three experiments. Furthermore, the simulations with different anthropogenic emission inventories do differ in their predictions of daily PM2.5, PM10, SO₂, and NO₂ concentrations with mean variations of 14%, 15%, 51%, and 16%, and maximum variations of 69%, 78%, 137%, and 132% over Zhejiang Province. The variations of monthly average concentrations of pollutants are basically consistent with those of daily average values of pollutants, but the maximum variations of simulated monthly values are obviously lower than those of simulated daily values. Generally, the variability of PM2.5, PM10, and NO₂ in the three simulations is significantly smaller than that of SO₂.

Abstract:Based on the observational soil moisture data collected at 778 agrometeorological stations in China from 1992 to 2010, ERA-Interim reanalysis soil moisture data, JRA55 reanalysis soil moisture data, NCEP-DOE R2 soil moisture data, and Twentieth Century Reanalysis (20CR) data, four statistical quantities, i.e., mean bias, correlation coefficient, standard deviation of differences, and ratio of standard deviations were calculated first. The applicability of these four reanalysis soil moisture datasets over eastern Northwest China, North China, and Jianghuai region were then investigated based on the four quantities by using the Brunke ranking method and the empirical orthogonal function analysis (EOF). Major conclusions are as follows. In the spring and summer, the JRA55 data is drier in eastern Northwest China with the seasonal average deviations at most stations are between -0.08 m³ m^-3 to 0.08 m³ m^-3. The soil moisture content in ERA-Interim, NCEP-DOE R2, 20CR is larger than observations; the average deviations in southern North China and Jianghuai region are less than the average deviations in northern North China and eastern Northwest China. For the interannual variability, the ERA-Interim reanalysis data agrees best with the observational data; it also best reproduces the variation tendency of the observed soil moisture in eastern Northwest China, North China and Jianghuai region. Overall, the ERA-Interim reanalysis data shows the best relationship with the observed soil moisture data, followed by the JRA55, NCEP-DOE R2 data, and the 20CR data is the worst.

Abstract:Although deterministic forecasting is not the main purpose and application of ensemble prediction system (EPS), the forecasting performance of each individual member determines the capability of the entire EPS and the ensemble mean is also an important reference index for the actual forecasting application. Therefore, using the EPS precipitation forecast data from 2013 to 2015 (from May to October every year) from the European Centre for Medium Range Weather Forecast (ECMWF) and hourly precipitation data from CMORPH (NOAA Climate Prediction Center Morphing Method) in combination with observations collected at more than thirty thousands of automatic weather stations in China, the performance of control forecast, member forecast, and ensemble mean of ECMWF ensemble prediction system on daily precipitation in the surrounding areas of Qinling Mountains are analyzed. The effective method to improve the performance of the ensemble mean of precipitation forecast is explored. Major conclusions are as follows. (1) The spatial pattern of precipitation in the surrounding areas of Qinling Mountains is well described by the ensemble mean and the control forecast. Comparatively, the control forecast can better represent the variance of precipitation. (2) Taylor analysis shows that the precipitation variance of the ensemble mean decreases monotonously with increases in the valid period of forecast, while the variance of control forecast shows less oscillations than that of the ensemble mean and the correlation coefficient improves slightly. (3) The forecast skill scores indicate that the ensemble mean yields significantly large bias (precipitation frequency) in light rain forecast, which indicates large false alarm rate for light precipitation; meanwhile, ensemble mean decreases the bias (precipitation frequency) in heavy rain forecast, suggesting that the missing rate for heavy precipitation forecast is high. As a result, TS and ETS scores of the ensemble mean tend to be lower than those of the control forecast and disturbed member prediction, which is attributed to the skewness distribution of precipitation. (4) The significant contribution of the ensemble mean lies in its ability to well predict the spatial location of possible precipitation. By limiting the threshold, adjusting the forecast bias, decreasing (increasing) the forecast frequency on light (heavy) rain, TS, and ETS scores of the ensemble mean can be improved obviously and the ensemble forecast skill would be superior to that of the member forecast and control forecast. At present, the forecast bias correction method has been successfully applied to the fine-resolution forecast system in Shaanxi.

Abstract:Numerical simulations with application of the nudging technique in the Weather Research and Forecasting (WRF) model to assimilate Final Operational Global Analysis data (FNL) were implemented to study three convective rainfall processes in Northwest China. Impacts of spectral nudging (SN) and grid nudging (GN) methods on precipitation simulation in this area were also examined. The result shows that the simulations of SN and GN experiments agree better with station-observed data compared with that of the control experiment. It is obvious that the simulation of distribution and magnitude of precipitation have been improved significantly in the two experiments with nudging, while the SN experiment results are better than that of the GN experiment. Analysis of differences in the basic meteorological elements (humidity, temperature, and wind speed) during the precipitation period simulated in the two assimilation experiments, it was found that the surface wind speed and temperature simulated by the GN experiment are closer to observations than that simulated by the SN experiment. However, the simulation of wind speed and relative humidity at 700 hPa and precipitation in the SN experiment are better than that in the GN experiment. Finally, from the perspective of the vertical variation of the diagnostic physical variable, i.e. water vapor flux divergence, the positive and negative distributions of water vapor flux divergence at 700 hPa and 600 hPa in the SN experiment effectively modulate the spatial distribution of precipitation and the simulation agrees well with observation. The increase of water vapor flux at 700 hPa is one of the major reasons for the improvement of precipitation simulation.

Abstract:In the present study, the NCEP/NCAR reanalysis monthly mean data and the rainfall data collected at 83 stations in Xinjiang are used to analyze the possible influences of the thermal contrast between Central Asia and South Asia on summer rainfall over the Tarim Basin. The results show that the summer rainfall over the Tarim Basin is closely related to temperature in the middle and lower troposphere over Central Asia and South Asia. When positive and negative temperature anomalies occur in middle and lower troposphere over South Asia and Central Asia respectively, an anomalous cyclone over Central Asia and an anomalous anticyclone over the Mongolia at 500 hPa will develop, resulting in southerly wind anomalies that prevail over the Tarim Basin, which is favorable for the transport of warm, moist air from the low latitude regions. At the same time, the anomalous anticyclones over the Arabian Sea and central Asia lead to the two-step transport of water vapor from the Arabian Sea to Central Asia and the Tarim Basin. The temperature in the middle and lower troposphere over central Asia mainly affects the atmospheric circulation at 500 hPa especially over Central Asia, and the temperature in the middle and lower troposphere over South Asia plays a more important role in water vapor transport. Further analysis indicates that the enhanced summer monsoon over the Tibetan Plateau corresponds to anomalous anticyclone at 600 hPa over the northern side of the Tibetan Plateau; this anomalous anticyclone is favorable for the southward movement of cold air from high latitudes, which leads to the middle and lower tropospheric cooling over central Asia. The significant sea surface temperature (SST) warming has a close relation with temperature change in the middle and lower troposphere over South Asia.

Abstract:Monthly mean sea surface temperature (SST) is characterized by non-stationary and nonlinear feature. It is obviously unreasonable to apply linear data processing methods directly to non-stationary and nonlinear time series, which would produce large prediction errors. In order to improve the prediction accuracy and better address the non-stationary and nonlinear sequence prediction problem, in this paper, we present an example based on monthly mean SST anomalies (SSTA) of the Northeast Pacific (40°N-50°N, 150°W-135°W). We first use ensemble empirical mode decompose (EEMD) and complementary ensemble empirical mode decomposition (CEEMD) to decompose monthly mean SST into a series of Intrinsic Mode Function (IMF). BP (Back Propagation) neural network model is then utilized to predict each IMF. Finally, the forecast results of each IMF are reconstructed to obtain the predicted value of monthly mean SST. Results of the experiment indicate that the accuracy of CEEMD is better than that of EEMD, and CEEMD has improved the forecast accuracy based on BP neural network. Statistical analysis of the results of a series of experiments shows that this method is effective for SST prediction at the 1-year scale.

Abstract:The arid region of Northwest China is one of the regions with strong summer surface sensible heat flux transfer in Eurasia. Surface sensible heat flux over this arid region plays an important role in the variation of the East Asian monsoon system. However, large uncertainties in summer sensible heat flux over this region have been found in various reanalysis datasets, which hinders our exact understanding of its climatic influence on the East Asian monsoon system. Using the summer measurements from 2001 to 2014 at Dunhuang Gobi site, this paper evaluated summer sensible heat fluxes in four reanalysis datasets including NCEP/NCAR, NCEP/DOE, ERA-Interim, and JRA-55. Sensible heat flux observations at Dunhuang Gobi site were derived from routine meteorological observations combined with a reasonable surface flux parameterization scheme (Y08 scheme). The results indicate that the average summer sensible heat flux at this site is about 85.7 W m^-2, while its variation range is large due to the influence of local rainfall. There exist certain differences in the sensible heat flux among different reanalysis datasets. Compared with the observations, the sensible heat flux from the ERA-Interim is the best in both the magnitude and variation, and agrees well with observations when there is no local rainfall influence. The reasons for the uncertainty in sensible heat flux in the four reanalysis products and the differences between reanalysis and observations were investigated. Surface wind speed and differences between surface skin temperature and air temperature are associated with the specification of roughness and surface flux parameterization scheme. The difference between surface skin temperature and air temperature in the reanalysis products was much smaller than the observation (6.5 ℃) mainly due to the unrealistically high value of roughness and inappropriate parameterization scheme for the Gobi surface in these reanalysis products. The parameterization scheme of ERA-Interim is relatively better than those of others in that the difference between surface skin temperature and air temperature is close to observations, which results in a relatively reasonable estimation of sensible heat flux.

Abstract:As one of the most important air pollutants, tropospheric ozone (O₃) has great impacts on morphological characteristics and physiology and biochemistry of plants, and thus further changes global and regional carbon cycle, climate and environmental conditions. In the present study, the authors first systematically review the previous studies of site-level observations concerning the impact of O₃ on vegetation, including photosynthesis, stomatal conductance, leaf area, and yield or biomass. Advantages and disadvantages of existing ozone exposure indices and parameterization schemes are then analyzed. In addition, the authors introduce the use of these parameterization schemes in global dynamic vegetation models and earth system models, and their application in quantifying the O₃ impact in the Earth system. Finally, the authors discuss the problems in the existing studies and provide suggestions for further development.

Abstract:The relation between precipitation extremes (PEs) and temperature in Anhui Province on different time scales during 1998-2014 has been investigated. On the interannual scale, the relationship between the magnitude of PEs and temperature presents a south-north dipole pattern with positive (negative) correlation over northern (southern) Anhui. Similar results are detected in the relation between the frequency of PEs and temperature. The quantitative results show that the frequency (magnitude) of PEs over Anhui decreased (enhanced) by 13.7%/℃ (0.03%/℃) with the temperature change. This suggests that the frequency of PEs is more sensitive to temperature change. On the daily and hourly scales, the extreme precipitation increased with increasing temperature. Daily extreme precipitation exponentially increase with temperature at the Clausius-Clapeyron (C-C) scaling rate. A super-CC scaling rate exists for hourly extremes, with increases in extreme precipitation observed at the rate about double the CC scaling rate for temperature. More importantly, both the daily and hourly extreme precipitation are found to decrease at the higher end of local temperature variations. Furthermore, the relation between PEs and temperature has been examined at scales of different hours. With increases in the time scale, the decrease becomes more significant. This may be related with the fact that the PEs in one day may be dominated by short duration PEs at higher temperature.

Abstract:On the basis of the NCEP/NCAR reanalysis data and CMIP5 19 model results, this work examined the performance of 19 CMIP5 models in the simulation of temporal variability and spatial pattern of the Arctic Oscillation (AO), and estimated the future changes of AO under two typical Representative Concentration Pathway scenarios (i.e., RCP4.5 and RCP8.5). Results show that most of the models can capture the basic structure of the AO mode, while MPI-ESM-LR and HadGEM2-AO can better simulate the overall AO mode than other models. However, some models overestimate the anomalous distribution on the Pacific side. Regarding the time series and temporal variability, the first mode of principal component (PC1) of the CMIP5 models basically can reproduce the weakening trend since 1950-1970, but the growing trend after 1970 is not obvious in the simulations. Nevertheless, the zonal index (ZI) sequence can simulate the trend in the two stages. On the whole, most of the PC1 and ZI sequences during 1950-2005 show a positive trend. More than half of the CMIP5 models can well simulate the high frequency cycles with 2-3 a; however, the simulations of 20-aquasi-periodic oscillation are poor. Among all the models, only CanESM2, CNRM-CM5, and GFDL-ESM2G can better simulate the ZI cycle inversion. It is found that the ZI sequence shows a significant upward trend under the RCP4.5 and RCP8.5 scenarios, and the interdecadal variation is obvious with three different stages, i.e., 2006-2039 and 2070-2001 correspond to two rising phases, while 2040-2069 is the slow descending phase. A majority of the simulation results reveals a positive trend, and more than 10 out of the 19 CMIP5 models have passed the test under both concentration scenarios.