Tea is a vital agricultural product in Taiwan. Due to global warming, the increasing extreme weather events have disrupted tea garden conditions and caused economic losses in agriculture. To address these challenges, a comprehensive tea garden risk assessment model, a Bayesian network (BN), was developed by considering various factors, including meteorological data, disaster events, tea garden environment (location, altitude, tea tree age, and soil characteristics), farming practices, and farmer interviews, and constructed risk assessment indicators for tea gardens based on the climate change risk analysis concept from the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). The results demonstrated an accuracy of over 92% in both validating and testing the model for tea tree damage and yield reduction. Sensitivity analysis revealed that tea tree damage and yield reduction were mutually influential, with weather, fertilization, and irrigation also impacting tea garden risk. Risk analysis under climate change scenarios from various global climate models (GCMs) indicated that droughts may pose the highest risk with up to 41% and 40% of serious tea tree growth damage and tea yield reduction, respectively, followed by cold events that most tea gardens may have less than 20% chances of serious impacts on tea tree growth and tea yield reduction. The impacts of heavy rains get the least concern because all five tea gardens may not be affected in terms of tea tree growth and tea yield with large chances of 67 to 85%. Comparing farming methods, natural farming showed lower disaster risk than conventional and organic approaches. The tea plantation risk assessment model can serve as a valuable resource for analyzing and offering recommendations for tea garden disaster management and is used to assess the impact of meteorological disasters on tea plantations in the future.

A version of the Community Earth System Model modified at the North Carolina State University (CESM-NCSU) is used to simulate the current and future atmosphere following the representative concentration partway scenarios for stabilization of radiative forcing at 4.5 W m(-2) (RCP4.5) and radiative forcing of 8.5 W m(-2) (RCP8.5). Part I describes the results from a comprehensive evaluation of current decadal simulations. Radiation and most meteorological variables are well simulated in CESM-NCSU. Cloud parameters are not as well simulated due in part to the tuning of model radiation and general biases in cloud variables common to all global chemistry-climate models. The concentrations of most inorganic aerosol species (i.e., SO42-, NH4+, and NO3-) are well simulated with normalized mean biases (NMBs) typically less than 20%. However, some notable exceptions are European NH4+, which is overpredicted by 33.0-42.2% due to high NH3 emissions and irreversible coarse mode condensation, and Cl-, that is negatively impacted by errors in emissions driven by wind speed and overpredicted HNO3. Carbonaceous aerosols are largely underpredicted following the RCP scenarios due to low emissions of black carbon, organic carbon, and anthropogenic volatile compounds in the RCP inventory and efficient wet removal. This results in underpredictions of PM2.5 and PM10 by 6.4-55.7%. The column mass abundances are reasonably well simulated. Larger biases occur in surface mixing ratios of trace gases in CESM-NCSU, likely due to numerical diffusion from the coarse grid spacing of the CESM-NCSU simulations or errors in the magnitudes and vertical structure of emissions. This is especially true for SO2 and NO2. The mixing ratio of O-3 is overpredicted by 38.9-76.0% due to the limitations in the O-3 deposition scheme used in CESM and insufficient titration resulted from large underpredictions in NO2. Despite these limitations, CESM-NCSU reproduces reasonably well the current atmosphere in terms of radiation, clouds, meteorology, trace gases, aerosols, and aerosol-cloud interactions, making it suitable for future climate simulations. (C) 2016 Elsevier Ltd. All rights reserved.

The uncertain, future development of emissions of short-lived trace gases and aerosols forms a key factor for future air quality and climate forcing. The Representative Concentration Pathways (RCPs) only explore part of this range as they all assume that worldwide ambitious air pollution control policies will be implemented. In this study, we explore how different assumptions on future air pollution policy and climate policy lead to different concentrations of air pollutants for a set of RCP-like scenarios developed using the IMAGE model. These scenarios combine low and high air pollution variants of the scenarios with radiative forcing targets in 2100 of 2.6 W m(-2) and 6.0 W m(-2). Simulations using the global atmospheric chemistry and transport model TM5 for the present-day climate show that both climate mitigation and air pollution control policies have large-scale effects on pollutant concentrations, often of similar magnitude. If no further air pollution policies would be implemented, pollution levels could be considerably higher than in the RCPs, especially in Asia. Air pollution control measures could significantly reduce the warming by tropospheric ozone and black carbon and the cooling by sulphate by 2020, and in the longer term contribute to enhanced warming by methane. These effects tend to cancel each other on a global scale. According to our estimates the effect of the worldwide implementation of air pollution control measures on the total global mean direct radiative forcing in 2050 is +0.09 W m(-2) in the 6.0 W m(-2) scenario and -0.16 W m(-2) in the 2.6 W m(-2) scenario. (C) 2013 Elsevier Ltd. All rights reserved.