The Advanced Research version of Weather Research and Forecasting (WRF‐ARW) model was used to generate a downscaled, 10‐km resolution regional climate dataset over the Red Sea and adjacent region. The model simulations are performed based on two, two‐way nested domains of 30‐ and 10‐km resolutions assimilating all conventional observations using a cyclic three‐dimensional variational approach over an initial 12‐h period. The improved initial conditions are then used to generate regional climate products for the following 24 h. We combined the resulting daily 24‐h datasets to construct a 15‐year Red Sea atmospheric downscaled product from 2000 to 2014. This 15‐year downscaled dataset is evaluated via comparisons with various in situ and gridded datasets. Our analysis indicates that the assimilated model successfully reproduced the spatial and temporal variability of temperature, wind, rainfall, relative humidity and sea level pressure over the Red Sea region. The model also efficiently simulated the seasonal and monthly variability of wind patterns, the Red Sea Convergence Zone and associated rainfall. Our results suggest that dynamical downscaling and assimilation of available observations improve the representation of regional atmospheric features over the Red Sea compared to global analysis data from the National Centers for Environmental Prediction. We use the dataset to describe the atmospheric climatic conditions over the Red Sea region.

The Red Sea is a narrow, elongated basin that is more than 2000 km long. This deceivingly simple structure offers very interesting challenges for wind and wave modeling, not easily, if ever, found elsewhere. Using standard meteorological products and local wind and wave models, this study explores how well the general and unusual wind and wave patterns of the Red Sea could be reproduced. The authors obtain the best results using two rather opposite approaches: the high-resolution Weather Research Forecasting (WRF) local model and the slightly enhanced surface winds from the global European Centre for Medium-Range Weather Forecasts model. The reasons why these two approaches produce the best results and the implications on wave modeling in the Red Sea are discussed. The unusual wind and wave patterns in the Red Sea suggest that the currently available wave model source functions may not properly represent the evolution of local fields. However, within limits, the WAVEWATCH III wave model, based on Janssen's and also Ardhuin's wave model physics, provides very reasonable results in many cases. The authors also discuss these findings and outline related future work.

This study presents a high-resolution spatial and temporal assessment of the solar energy resources over the Arabian Peninsula (AP) from 38 years reanalysis data generated using an assimilative Weather Research and Forecasting Solar model. The simulations are performed based on two, two-way nested domains with 15 km and 5 km resolutions using the European Centre for Medium-Range Weather Forecasts as initial and boundary conditions and assimilating most of available observations in the region. Simulated solar energy resources, such as the Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and the Diffusive Horizontal Irradiance (DHI), are first validated with daily observations collected at 46 in-situ radiometer stations over Saudi Arabia for a period of four years (2013)(2014)(2015)(2016). Observed and modelled data are in good agreement with high correlation coefficients, index of agreements, and low normalized biases.The total mean annual GHI (DNI) over the AP ranges from 6000 to 8500 Wh m −2 (3000 to 6500 Wh m −2 ) with significant seasonal variations. The diffuse fraction (the ratio of the DHI to the GHI) is high (low) over the northern (southern) AP in winter whereas it is high (low) over the central to southern (northern) AP during summer, indicating a significant modulation of the sky clearness over the region. Clouds over the northern AP in winter and the aerosol loading due to desert dust over the central and southern AP in summer are the major factors driving the variability of the DHI. The effects of dust and clouds are more pronounced in the diurnal variability of the solar radiation parameters. Our analysis of various solar radiation parameters and the aerosol properties suggest a significant potential for solar energy harvesting in the AP. In particular, the southeastern to northwestern Saudi Arabia are identified as the most suitable areas to exploit solar energy with a minimum cloud coverage over the region.

The Red Sea convergence zone (RSCZ) is formed by opposite surface winds blowing from northwest to southeast directions at around 18 ∘ -19 ∘ N between October and January. A reverse-oriented, low-level monsoon trough at 850 hPa, known as the Red Sea trough (RST), transfers moisture from the southern Red Sea to RSCZ. The positions of the RSCZ and RST and the intensity of the RST have been identified as important factors in modulating weather and climatic conditions across the Middle East. Here, we investigate the influence of the El Niño southern oscillation (ENSO) on the interannual variability of RSCZ, RST, and regional rainfall during winter months. Our results indicate that El Niño (warm ENSO phase) favours a shift of the RSCZ to the north and a strengthening of the RST in the same direction. Conversely, during November and December of La Niña periods (cold ENSO phase), the RSCZ shift to the south and the RST strengthens in the same direction. During El Niño periods, southeasterly wind speeds increase (20-30%) over the southern Red Sea and northwesterly wind speeds decrease (10-15%) over the northern Red Sea. Noticeable increases in the number of rainy days and the intensity of rain events are observed during El Niño phases. These increases are associated with colder than normal air intrusion at lower levels from the north combined with warm air intrusion from the south over the RSCZ. Our analysis suggests that during El Niño winters, warmer sea surface temperatures and higher convective instability over the Red Sea favour local storms conditions and increase rainfall over the Red Sea and adjoining regions.

Capsule Summary An integrated, high resolution, data-driven regional modeling system has been recently developed for the Red Sea region and is being used for research and various environmental applications.

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Abstract. The local weather and climate of the Himalayas are sensitive and interlinked with global-scale changes in climate, as the hydrology of this region is mainly governed by snow and glaciers. There are clear and strong indicators of climate change reported for the Himalayas, particularly the Jammu and Kashmir region situated in the western Himalayas. In this study, using observational data, detailed characteristics of long- and short-term as well as localized variations in temperature and precipitation are analyzed for these six meteorological stations, namely, Gulmarg, Pahalgam, Kokarnag, Qazigund, Kupwara and Srinagar during 1980–2016. All of these stations are located in Jammu and Kashmir, India. In addition to analysis of stations observations, we also utilized the dynamical downscaled simulations of WRF model and ERA-Interim (ERA-I) data for the study period. The annual and seasonal temperature and precipitation changes were analyzed by carrying out Mann–Kendall, linear regression, cumulative deviation and Student's t statistical tests. The results show an increase of 0.8 ∘C in average annual temperature over 37 years (from 1980 to 2016) with higher increase in maximum temperature (0.97 ∘C) compared to minimum temperature (0.76 ∘C). Analyses of annual mean temperature at all the stations reveal that the high-altitude stations of Pahalgam (1.13 ∘C) and Gulmarg (1.04 ∘C) exhibit a steep increase and statistically significant trends. The overall precipitation and temperature patterns in the valley show significant decreases and increases in the annual rainfall and temperature respectively. Seasonal analyses show significant increasing trends in the winter and spring temperatures at all stations, with prominent decreases in spring precipitation. In the present study, the observed long-term trends in temperature (∘Cyear-1) and precipitation (mm year−1) along with their respective standard errors during 1980–2016 are as follows: (i) 0.05 (0.01) and −16.7 (6.3) for Gulmarg, (ii) 0.04 (0.01) and −6.6 (2.9) for Srinagar, (iii) 0.04 (0.01) and −0.69 (4.79) for Kokarnag, (iv) 0.04 (0.01) and −0.13 (3.95) for Pahalgam, (v) 0.034 (0.01) and −5.5 (3.6) for Kupwara, and (vi) 0.01 (0.01) and −7.96 (4.5) for Qazigund. The present study also reveals that variation in temperature and precipitation during winter (December–March) has a close association with the North Atlantic Oscillation (NAO). Further, the observed temperature data (monthly averaged data for 1980–2016) at all the stations show a good correlation of 0.86 with the results of WRF and therefore the model downscaled simulations are considered a valid scientific tool for the studies of climate change in this region. Though the correlation between WRF model and observed precipitation is significantly strong, the WRF model significantly underestimates the rainfall amount, which necessitates the need for the sensitivity study of the model using the various microphysical parameterization schemes. The potential vorticities in the upper troposphere are obtained from ERA-I over the Jammu and Kashmir region and indicate that the extreme weather event of September 2014 occurred due to breaking of intense atmospheric Rossby wave activity over Kashmir. As the wave could transport a large amount of water vapor from both the Bay of Bengal and Arabian Sea and dump them over the Kashmir region through wave breaking, it probably resulted in the historical devastating flooding of the whole Kashmir valley in the first week of September 2014. This was accompanied by extreme rainfall events measuring more than 620 mm in some parts of the Pir Panjal range in the south Kashmir.

The structure and climatology of the monsoon low‐level jet (MLLJ) is studied based on dynamically downscaled simulations over a 37‐year period (1980–2016) using the Weather Research Forecasting (WRF) model. The simulations are conducted by adopting a continuous initialization method with daily re‐initializations using ERA‐Interim data as initial and boundary conditions. Validation of the downscaled fields with radiosonde data shows that the model has reasonable skill in reproducing MLLJ characteristics. Analysis of the simulations suggests that the MLLJ exhibits systematic diurnal variation: maximum winds of the synoptically induced large‐scale monsoon jet occur during the daytime, and the orographic channelled winds through the mountains of East Africa, Hejaz, and Western Ghats in the night‐time. These diurnal changes in monsoon winds modulate the moisture convergence process and the associated evolution of rainfall over India. Seasonal and monthly climatology of monsoon winds shows that the model accurately reproduces the spatial pattern of winds and slightly overestimates (2–3 m/s) the mean monthly winds over the Bay of Bengal and Arabian Seas. Analysis of wind variability and the trends using 37 years simulations suggests that the MLLJ exhibits an increasing trend in wind speed on both seasonal and monthly scales, except for August which shows a decreasing trend. The weakening of the MLLJ in August has a profound influence on the number of monsoon depressions forming over the Bay of Bengal (which are decreasing), and on the number of break days (which are increasing) and associated precipitation reduction over the central Indian region.