Abstract. The implementation of emission reductions during the 2014 Asia-Pacific Economic Cooperation (APEC) summit provides a valuable opportunity to study air pollution in Beijing. From 15 October to 30 November 2014, the height of the atmospheric mixing layer and the vertical attenuated backscattering coefficient profiles were observed online using a lidar ceilometer. Compared with fine particulate matter (PM 2.5 ) and aerosol optical depth (AOD) data, the attenuated backscattering coefficients measured by the lidar ceilometer were strongly correlated with the PM 2.5 concentration and AOD (correlation coefficients of 0.89 and 0.86, respectively). This result demonstrated the reliability of the vertical distribution of particles measured by the lidar ceilometer. By classifying different degrees of air pollution based on visibility, we found that during the transition period of air pollution, which was affected by transport of southerly flows in the mixing layer, the attenuated backscattering coefficient from 0 to 1500 m was enhanced by approximately 1.4 Mm −1 sr −1 (140 %). During the polluted period, the attenuated backscattering coefficient from 0 to 300 m suddenly increased, and the coefficient near the surface peaked (approximately 14 Mm −1 sr −1 ); however, the attenuated backscattering coefficient from 300 to 900 m decreased gradually, and the average value from 0 to 1500 m decreased by 0.5 Mm −1 sr −1 (20 %). The height of the mixing layer gradually decreased, and the ratio of CO / SO 2 gradually increased, which indicate that the polluted period was dominated by local contribution. Due to the emission reductions during APEC (DAPEC), the concentration of PM 2.5 decreased by 59.2 and 58.9 % and visibility improved by 70.2 and 56.0 % compared to before (BAPEC) and after APEC (AAPEC), respectively. The contribution of regional transport in DAPEC decreased by approximately 36 and 25 %, and the local contribution decreased by approximately 48 and 54 % compared to BAPEC and AAPEC, respectively. The most effective method of controlling air pollution in the Beijing area is to reduce regional emissions during the transition period and reduce local emissions during the polluted period.
Abstract. Dynamically downscaled precipitation fields from regional climate models (RCMs) often cannot be used directly for regional climate studies. Due to their inherent biases, i.e., systematic over-or underestimations compared to observations, several correction approaches have been developed. Most of the bias correction procedures such as the quantile mapping approach employ a transfer function that is based on the statistical differences between RCM output and observations. Apart from such transfer functionbased statistical correction algorithms, a stochastic bias correction technique, based on the concept of Copula theory, is developed here and applied to correct precipitation fields from the Weather Research and Forecasting (WRF) model. For dynamically downscaled precipitation fields we used high-resolution (7 km, daily) WRF simulations for Germany driven by ERA40 reanalysis data for 1971-2000. The REG-NIE (REGionalisierung der NIEderschlagshöhen) data set from the German Weather Service (DWD) is used as gridded observation data (1 km, daily) and aggregated to 7 km for this application. The 30-year time series are split into a calibration (1971-1985) and validation (1986-2000) period of equal length. Based on the estimated dependence structure (described by the Copula function) between WRF and REGNIE data and the identified respective marginal distributions in the calibration period, separately analyzed for the different seasons, conditional distribution functions are derived for each time step in the validation period. This finally allows to get additional information about the range of the statistically possible bias-corrected values. The results show that the Copula-based approach efficiently corrects most of the errors in WRF derived precipitation for all seasons. It is also found that the Copula-based correction performs better for wet bias correction than for dry bias correction. In autumn and winter, the correction introduced a small dry bias in the northwest of Germany. The average relative bias of daily mean precipitation from WRF for the validation period is reduced from 10 % (wet bias) to −1 % (slight dry bias) after the application of the Copula-based correction. The bias in different seasons is corrected from 32 % March-April-May (MAM), −15 % June-July-August (JJA), 4 % September-October-November (SON) and 28 % December-January-February (DJF) to 16 % (MAM), −11 % (JJA), −1 % (SON) and −3 % (DJF), respectively. Finally, the Copula-based approach is compared to the quantile mapping correction method. The root mean square error (RMSE) and the percentage of the corrected time steps that are closer to the observations are analyzed. The Copula-based correction derived from the mean of the sampled distribution reduces the RMSE significantly, while, e.g., the quantile mapping method results in an increased RMSE for some regions.
Abstract. Global water models (GWMs) simulate the terrestrial water cycle on the global scale and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modelling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how 16 state-of-the-art GWMs are designed. We analyse water storage compartments, water flows, and human water use sectors included in models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to enhance model intercomparison, improvement, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Six models used six compartments, while four models (DBH, JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water for the irrigation sector. We conclude that, even though hydrological processes are often based on similar equations for various processes, in the end these equations have been adjusted or models have used different values for specific parameters or specific variables. The similarities and differences found among the models analysed in this study are expected to enable us to reduce the uncertainty in multi-model ensembles, improve existing hydrological processes, and integrate new processes.
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