In this study four mesoscale forecasting systems were used to investigate the four-dimensional structure of atmospheric refractivity and ducting layers that occur within evolving synoptic conditions over the eastern seaboard of the United States. The aim of this study was to identify the most important components of forecasting systems that contribute to refractive structures simulated in a littoral environment. Over a 7-day period in April-May of 2000 near Wallops Island, Virginia, meteorological parameters at the ocean surface and within the marine atmospheric boundary layer (MABL) were measured to characterize the spatiotemporal variability contributing to ducting. By using traditional statistical metrics to gauge performance, the models were found to generally overpredict MABL moisture, resulting in fewer and weaker ducts than were diagnosed from vertical profile observations. Mesoscale features in ducting were linked to highly resolved sea surface temperature forcing and associated changes in surface stability and to local variations in internal boundary layers that developed during periods of offshore flow. Sensitivity tests that permit greater mesoscale detail to develop on the model grids revealed that initialization of the simulations and the resolution of sea surface temperature analyses were critical factors for accurate predictions of coastal refractivity.

of the RCMs are often different than the driving GCMs and arguably more credible given the improved performance of the RCM. This also suggests that local climate forcing will be a significant driver of the regional response to climate change over Africa.

To estimate potential impact of climate change on wheat fusarium ear blight, simulated weather for the A1B climate change scenario was imported into a model for estimating fusarium ear blight in central China. In this work, a logistic weather-based regression model for estimating incidence of wheat fusarium ear blight in central China was developed, using up to 10 years (2001-2010) of disease, anthesis date and weather data available for 10 locations in Anhui and Hubei provinces. In the model, the weather variables were defined with respect to the anthesis date for each location in each year. The model suggested that incidence of fusarium ear blight is related to number of days of rainfall in a 30-day period after anthesis and that high temperatures before anthesis increase the incidence of disease. Validation was done to test whether this relationship was satisfied for another five locations in Anhui province with fusarium ear blight data for 4 to 5 years but no nearby weather data, using weather data generated by the regional climate modelling system PRECIS. How climate change may affect wheat anthesis date and fusarium ear blight in central China was investigated for period 2020-2050 using wheat growth model Sirius and climate data generated by PRECIS. The projection suggested that wheat anthesis dates will generally be earlier and fusarium ear blight incidence will increase substantially for most locations

No abstract

Radar ducting is caused by sharp vertical changes in temperature and, especially, water vapor at the top of the atmospheric boundary layer, both of which are sensitive to variations in the underlying surface conditions, local mesoscale weather, and synoptic weather patterns. High-resolution numerical weather prediction (NWP) models offer an alternative to observation to determine boundary layer (BL) structure and to assess the spatial variability of radar ducts. The benefit of using NWP models for simulating ducting conditions very much depends on the initial state of sea surface temperature (SST) and the model spinup time, both of which have a great impact on BL structure. This study investigates the effects of variation of NWP-model initial conditions and simulation length on the accuracy of simulating the atmosphere's refractive index over the Wallops Island, Virginia, region, which has pronounced SST variability and complex BL structure. The Met Office Unified Model (MetUM) with horizontal resolution of 4 km (4-km model) was used to simulate the atmospheric environment when observations were made during the Wallops-2000 experiment. Sensitivity tests were conducted in terms of the variability of SST and model spinup time. The evaluation of the model results was focused on low-level moisture, temperature, and surface ducting characteristics including the frequency, strength, and the height of the ducting layer. When provided with more accurate SST and adequate simulation length, the MetUM 4-km model demonstrated an improved ability to predict the observed ducting.

In aiming for better access to climate change information and for providing climate service, it is important to obtain reliable high-resolution temperature simulations. Systematic comparisons are still deficient between statistical and dynamic downscaling techniques because of their inherent unavoidable uncertainties. In this paper, 20 global climate models (GCMs) and one regional climate model [Providing Regional Climates to Impact Studies (PRECIS)] are employed to evaluate their capabilities in reproducing average trends of mean temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), and extreme events represented by frost days (FD) and heat-wave days (HD) across China. It is shown generally that bias of temperatures from GCMs relative to observations is over ±1°C across more than one-half of mainland China. PRECIS demonstrates better representation of temperatures (except for HD) relative to GCMs. There is relatively better performance in Huanghuai, Jianghuai, Jianghan, south Yangzi River, and South China, whereas estimation is not as good in Xinjiang, the eastern part of northwest China, and the Tibetan Plateau. Bias-correction spatial disaggregation is used to downscale GCMs outputs, and bias correction is applied for PRECIS outputs, which demonstrate better improvement to a bias within ±0.2°C for Tm, Tmax, Tmin, and DTR and ±2 days for FD and HD. Furthermore, such improvement is also verified by the evidence of increased spatial correlation coefficient and symmetrical uncertainty, decreased root-mean-square error, and lower standard deviation for reproductions. It is seen from comprehensive ranking metrics that different downscaled models show the most improvement across different climatic regions, implying that optional ensembles of models should be adopted to provide sufficient high-quality climate information.

Many applications of risk assessment and Environmental

A high-quality rice activation tagging population has been developed and screened for drought-tolerant lines using various water stress assays. One drought-tolerant line activated two rice glutamate receptor-like genes. Transgenic overexpression of the rice glutamate receptor-like genes conferred drought tolerance to rice and Arabidopsis. Rice (Oryza sativa) is a multi-billion dollar crop grown in more than one hundred countries, as well as a useful functional genetic tool for trait discovery. We have developed a population of more than 200,000 activation-tagged rice lines for use in forward genetic screens to identify genes that improve drought tolerance and other traits that improve yield and agronomic productivity. The population has an expected coverage of more than 90 % of rice genes. About 80 % of the lines have a single T-DNA insertion locus and this molecular feature simplifies gene identification. One of the lines identified in our screens, AH01486, exhibits improved drought tolerance. The AH01486 T-DNA locus is located in a region with two glutamate receptor-like genes. Constitutive overexpression of either glutamate receptor-like gene significantly enhances the drought tolerance of rice and Arabidopsis, thus revealing a novel function of this important gene family in plant biology.