Elevation dependent precipitation and temperature changes over Indian Himalayan region

Various studies reported an elevation dependent precipitation and temperature changes in mountainous regions of the world including the Himalayas. Various mechanisms are proposed to link the possible dependence of the precipitation and temperature on elevation with other variables, including, long- and short-wave radiation, albedo, clouds, humidity, etc. In the present study changes and trends of precipitation and temperature at different elevation ranges in the Indian Himalayan region (IHR) is assessed. Observations and modelling fields during the period 1970–2099 are used. Modelling simulations from the Coordinated Regional Climate Downscaling Experiment-South Asia experiments (CORDEX-SA) suites are considered. In addition, four seasons—winter (Dec, Jan, Feb: DJF), pre-monsoon (Mar, Apr, May: MAM), monsoon (Jun, Jul, Aug, Sep: JJAS) and post-monsoon (Oct, Nov: ON)—are considered to detect the possible seasonal response of elevation dependency. Firstly, precipitation and temperature fields, separately, as well as the diurnal temperature range (DTR) are assessed. Following, their long-term trends are investigated, if varying, at different elevational ranges in the IHR. To explain plausible physical mechanisms due to elevation dependency, trend of other variables viz., surface downward longwave radiation (DLR), total cloud faction, soil moisture, near surface specific humidity, surface snow melt and surface albedo, etc. are investigated. Results point towards an decreased (increased) precipitation in higher (lower) elevation. And amplified warming signals at higher elevations (above 3000 m), both in daytime and nighttime temperatures, during all seasons except the monsoon, are noticed. Increased DLR trends at higher elevation are also simulated well by the model and are likely the main elevation dependent driver in the IHR

Mass-balance modelling of Gangotri glacier

The snow-covered area, for months Jan-Dec, obtained for Gangotri glacier using Landsat data and NDSI (normalized differencing snow index) for year 201

Estimation of snowfall limit for the Kashmir Valley, Indian Himalayas, with TRMM PR Bright Band information

Knowing the height of the snowfall limit during precipitation events is crucial for better understanding a number of hydro-climatic processes, for instance glacier-climate interactions or runoff from high mountain catchments. However, knowledge on heights of the phase change during precipitation events is limited by the small number of meteorological measurements available at high altitudes, such as the Himalayas. The bright band (BB) of satellite based radar data may be a promising proxy for the snow/rain transition during particular stratiform precipitation events over high mountain regions. The BB is a horizontal layer of stronger radar reflectivity caused by the melting of hydrometeors at the level where solid precipitation turns into rain. Here, we present BB heights detected by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) 2A23 algorithm over a mountainous area. To assess the performance of BB heights, we have compared a 17‑year data set of BB estimations of the TRMM PR with radiosonde observations and meteorological station data from Srinagar, Kashmir Valley, India. During March to November, the BB lies mostly about 200 to 800 m below the freezing level (FL) recorded by radiosondes. The correlation between BB and FL heights extrapolated from a ground-based station is smaller and depends on the timing of the air temperature measurement – an important finding for applying extrapolation techniques in data sparse regions. Further on, we found a strong seasonal and monthly variability of the BB height, e.g. extending in summer months from about 2700 m to almost 6000 m asl. Comparison with near surface rain intensity from the TRMM PR product 2A25 indicates that – during intense monsoonal summer precipitation events – the BB height is concentrated between about 3500 and 4000 m asl. We can conclude that TRMM PR BB data deliver valuable complementary information for regional or seasonal variability in snow/rain transition in data sparse regions and, further on, BB data from surrounding lowlands could be used to validate extrapolation approaches to assess snowfall limit for mainly stratiform precipitation events where stations at high elevations are missing

Regional projections of North Indian climate for adaptation studies

Adaptation is increasingly important for regions around the world where large changes in climate could have an impact on populations and industry. The Brahmaputra–Ganges catchments have a large population, a main industry of agriculture and a growing hydro-power industry, making the region susceptible to changes in the Indian Summer Monsoon, annually the main water source. The HighNoon project has completed four regional climate model simulations for India and the Himalaya at high resolution (25 km) from 1960 to 2100 to provide an ensemble of simulations for the region. In this paper we have assessed the ensemble for these catchments, comparing the simulations with observations, to give credence that the simulations provide a realistic representation of atmospheric processes and therefore future climate. We have illustrated how these simulations could be used to provide information on potential future climate impacts and therefore aid decision-making using climatology and threshold analysis. The ensemble analysis shows an increase in temperature between the baseline (1970–2000) and the 2050s (2040–2070) of between 2 and 4 °C and an increase in the number of days with maximum temperatures above 28 °C and 35 °C. There is less certainty for precipitation and runoff which show considerable variability, even in this relatively small ensemble, spanning zero. The HighNoon ensemble is the most complete data for the region providing useful information on a wide range of variables for the regional climate of the Brahmaputra–Ganges region, however there are processes not yet included in the models that could have an impact on the simulations of future climate. We have discussed these processes and show that the range from the HighNoon ensemble is similar in magnitude to potential changes in projections where these processes are included. Therefore strategies for adaptation must be robust and flexible allowing for advances in the science and natural environmental changes

Spatial and meteorological controls of stable water isotope dynamics of precipitation in Kashmir Valley, Western Himalaya, India

In the Himalayas, the lives and livelihoods of millions of people are sustained by water resources primarily depending on the moisture brought by Western Disturbances and Indian Summer Monsoon. In the present study, a network of 12 precipitation stations was established across the Kashmir Valley to understand the spatial and meteorological factors controlling precipitation isotopes. Temperature and relative humidity are dominant meteorological factors, whereas altitude, proximity to forest canopy, land use/land cover, windward and leeward sides of the mountains are the main physical factors influencing precipitation isotopes. The study suggests that the Mediterranean Sea and nearby water bodies along with continental recycling are the dominant sources of moisture from October to May, while the Arabian Sea, Bay of Bengal and continental recycling are the main sources of moisture from June to September. However, some precipitation events from October to May collect moisture from the Arabian Sea and some precipitation events from June to September collect moisture from the Mediterranean Sea. The occasional passage of Western Disturbances in summer merging with the Indian Summer Monsoon yields heavy to very heavy precipitation. The study provides a better understanding of complex spatial and meteorological phenomena controlling precipitation isotopes across the Western Himalayas.</p

Climate projections for glacier change modelling over the Himalayas

Glaciers are of key importance to freshwater supplies in the Himalayan region. Their growth or decline is among other factors determined by an interaction of 2‐meter air temperature (TAS) and precipitation rate (PR) and thereof derived positive degree days (PDD) and snow and ice accumulation (SAC). To investigate determining factors in climate projections, we use a model ensemble consisting of 36 CMIP5 General Circulation Models (GCMs) and 13 Regional Climate Models (RCMs) of two Asian CORDEX domains for two different representative concentration pathways (RCP4.5 and RCP8.5). First, we downsize the ensemble in respect to the models' ability to correctly reproduce dominant circulation patterns (i.e. the Indian summer monsoon (ISM) and Western Disturbances (WDs)) as well as elevation dependent trend signals in winter. Within this evaluation, a newly produced dataset for the Indus, Ganges and Brahmaputra catchments is used as observational data. The reanalyses WFDEI, ERA‐Interim, NCEP/NCAR and JRA‐55 are used to further account for observational uncertainty. In a next step, remaining TAS and PR data are bias corrected applying a new bias adjustment method, Scale Distribution Mapping and subsequently PDD and SAC computed. Finally, we identify and quantify projected climate change effects. Until the end of the century, the ensemble indicates a rise of PDD, especially during summer and for lower altitudes. Also TAS is rising, though the highest increases are shown for higher altitudes and between December and April (DJFMA). PRs connected to the ISM are projected to robustly increase, while signals for PR changes during DJFMA show a higher level of uncertainty and spatial heterogeneity. However, a robust decline in solid precipitation is projected over our research domain, with the exception of a small area in the high mountain Indus catchment where no clear signal emerges. This article is protected by copyright. All rights reserved