A better understanding of regional‐scale precipitation patterns in the Himalayan region is required to increase our knowledge of the impacts of climate change on downstream water availability. This study examines the impact of four cloud microphysical schemes (Thompson, Morrison, Weather Research and Forecasting (WRF) single‐moment 5‐class, and WRF double‐moment 6‐class) on summer monsoon precipitation in the Langtang Valley in the central Nepalese Himalayas, as simulated by the WRF model at 1 km grid spacing for a 10 day period in July 2012. The model results are evaluated through a comparison with surface precipitation and radiation measurements made at two observation sites. Additional understanding is gained from a detailed examination of the microphysical characteristics simulated by each scheme, which are compared with measurements using a spaceborne radar/lidar cloud product. Also examined are the roles of large‐ and small‐scale forcings. In general, the schemes are able to capture the timing of surface precipitation better than the actual amounts in the Langtang Valley, which are predominately underestimated, with the Morrison scheme showing the best agreement with the measured values. The schemes all show a large positive bias in incoming radiation. Analysis of the radar/lidar cloud product and hydrometeors from each of the schemes suggests that “cold‐rain” processes are a key precipitation formation mechanism, which is also well represented by the Morrison scheme. As well as microphysical structure, both large‐scale and localized forcings are also important for determining surface precipitation.

The Sichuan basin is one of the most densely populated regions of China, making the area particularly vulnerable to the adverse impacts associated with future climate change. As such, climate models are important for understanding regional and local impacts of climate change and variability, like heat stress and drought. In this study, climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are validated over the Sichuan basin by evaluating how well each model can capture the phase, amplitude, and variability of the regionally observed mean, maximum, and minimum temperature between 1979 and 2005. The results reveal that the majority of the models do not capture the basic spatial pattern and observed means, trends, and probability distribution functions. In particular, mean and minimum temperatures are underestimated, especially during the winter, resulting in biases exceeding −3°C. Models that reasonably represent the complex basin topography are found to generally have lower biases overall. The five most skillful climate models with respect to the regional climate of the Sichuan basin are selected to explore twenty-first-century temperature projections for the region. Under the CMIP5 high-emission future climate change scenario, representative concentration pathway 8.5 (RCP8.5), the temperatures are projected to increase by approximately 4°C (with an average warming rate of +0.72°C decade⁻¹), with the greatest warming located over the central plains of the Sichuan basin, by 2100. Moreover, the frequency of extreme months (where mean temperature exceeds 28°C) is shown to increase in the twenty-first century at a faster rate compared to the twentieth century.

The need to provide accurate estimates of precipitation over catchments in the Hindu Kush, Karakoram, and Himalaya mountain ranges for hydrological and water resource systems assessments is widely recognized, as is identifying precipitation extremes for assessing hydro‐meteorological hazards. Here, we investigate the ability of bias‐corrected Weather Research and Forecasting model output at 5‐km grid spacing to reproduce the spatiotemporal variability of precipitation for the Beas and Sutlej river basins in the Himalaya, measured by 44 stations spread over the period 1980 to 2012. For the Sutlej basin, we find that the raw (uncorrected) model output generally underestimated annual, monthly, and (particularly low‐intensity) daily precipitation amounts. For the Beas basin, the model performance was better, although biases still existed. It is speculated that the cause of the dry bias over the Sutlej basin is a failure of the model to represent an early‐morning maximum in precipitation during the monsoon period, which is related to excessive precipitation falling upwind. However, applying a nonlinear bias‐correction method to the model output resulted in much better results, which were superior to precipitation estimates from reanalysis and two gridded datasets. These findings highlight the difficulty in using current gridded datasets as input for hydrological modeling in Himalayan catchments, suggesting that bias‐corrected high‐resolution regional climate model output is in fact necessary. Moreover, precipitation extremes over the Beas and Sutlej basins were considerably underrepresented in the gridded datasets, suggesting that bias‐corrected regional climate model output is also necessary for hydro‐meteorological risk assessments in Himalayan catchments.

Understanding the local valley wind regimes in the Hindu‐Kush Karakoram Himalaya is vital for future predictions of the glacio‐hydro‐meteorological system. Here the Weather Research and Forecasting model is employed at a resolution of 1 km to investigate the forces driving the local valley wind regime in a river basin in the Nepalese Himalaya, during July 2013 and January 2014. Comparing with observations shows that the model represents the diurnal cycle of the winds well, with strong daytime up‐valley winds and weak nighttime winds in both months. A momentum budget analysis of the model output shows that in the summer run the physical drivers of the near‐surface valley wind also have a clear diurnal cycle, and are dominated by the pressure gradient, advection, and turbulent vertical mixing, as well as a nonphysical numerical diffusion term. By contrast, the drivers in the winter run have a less consistent diurnal cycle. In both months, the pressure gradient, advection, numerical diffusion, and Coriolis terms dominate up to 5,000 m above the ground. The drivers are extremely variable over the valley, and also influenced by the presence of glaciers. When glaciers are removed from the model in the summer run, the wind continues further up the valley, indicating how the local valley winds might respond to future glacier shrinkage. The spatial variability of the drivers over both months is consistent with the complex topography in the basin, which must therefore be well represented in weather and regional climate models to generate accurate outputs.

Despite the recent significant climatic changes observed over West Antarctica, which include large warming in central West Antarctica and accelerated ice loss, adequate validation of regional simulations of meteorological variables are rare for this region. To address this gap, results from a recent version of the Polar Weather Research and Forecasting model (Polar WRF) covering West Antarctica at a high horizontal resolution of 5 km were validated against near‐surface meteorological observations. The model employed physics options that included the Mellor‐Yamada‐Nakanishi‐Niino boundary layer scheme, the WRF Single Moment 5‐Class cloud microphysics scheme, the new version of the rapid radiative transfer model for both shortwave and longwave radiation, and the Noah land surface model. Our evaluation finds this model to be a useful tool for realistically capturing the near‐surface meteorological conditions. It showed high skill in simulating surface pressure (correlation ≥0.97), good skill for wind speed with better correlation at inland sites (0.7–0.8) compared to coastal sites (0.3–0.6), generally good representation of strong wind events, and good skill for temperature in winter (correlation ≥0.8). The main shortcomings of this configuration of Polar WRF are an occasional failure to properly represent transient cyclones and their influence on coastal winds, an amplified diurnal temperature cycle in summer, and a general tendency to underestimate the wind speed at inland sites in summer. Additional sensitivity studies were performed to quantify the impact of the choice of boundary layer scheme and surface boundary conditions. It is shown that the model is most sensitive to the choice of boundary layer scheme, with the representation of the temperature diurnal cycle in summer significantly improved by selecting the Mellor‐Yamada‐Janjic boundary layer scheme. By contrast, the model results showed little sensitivity to whether the horizontal resolution was 5 or 15 km.

On 30th January 1982, the research station on Signy Island (South Orkney Islands) reported a daily maximum temperature of 19.8 °C. This is a record maximum for any station south of 60°S. We use surface observations, atmospheric reanalyses and high‐resolution atmospheric model simulations to investigate the drivers of this extreme event. At the time of the record temperature exceptionally warm air was being advected southwards towards the South Orkney Islands from the subtropical South Atlantic. This air mass cooled significantly at levels below 1 km during its long track over the cold Southern Ocean but remained relatively warm above this level. Atmospheric model simulations show that warm air from upper levels was brought down towards the surface over Signy Island in a föhn wind generated by northerly flow over Coronation Island, a mountainous island just to the north of Signy Island. Modelled temperatures over Signy Island are in good agreement with observations and thus support the hypothesis that the record temperature was caused by a combination of exceptional warm advection with conditions suitable for the generation of föhn. Since conditions conducive to föhn occur relatively frequently, föhn warming may have a significant influence on the local climate and ecology of Signy Island.