Future changes over the Himalayas: Maximum and minimum temperature

Dimri, A. P.; Kumar, D. Nagesh; Choudhary, Anubhav; Maharana, P.

doi:10.1016/j.gloplacha.2018.01.015

Cited by 60 publications

(26 citation statements)

References 110 publications

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“…This general significant increasing trend of temperature in future climate scenarios has been reported in climate impact analyses in other high mountain regions, like the Alps and the Himalayas [73][74][75][76], as well as in other semiarid mountain areas in Chile (the Andes) and in California (the Sierra Nevada) [77][78][79]. Similar values of the estimated trends for mean temperature have been found for the Alps (0.22 and 0.58 • C•decade −1 for RCPs 4.5 and 8.5, respectively), Sierra Nevada (0.18 and 0.46 • C•decade −1 for RCPs 4.5 and 8.5, respectively), and Chile (where an increase of 3 to 4 • C for the 21st century has been shown) [77,78].…”

Section: Discussionsupporting

confidence: 69%

Climate Trends Impact on the Snowfall Regime in Mediterranean Mountain Areas: Future Scenario Assessment in Sierra Nevada (Spain)

Pérez-Palazón

Pimentel

Polo

2018

Water

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Snow constitutes a key component of the water cycle, which is directly affected by changes in climate. Mountainous regions, especially those located in semiarid environments, are highly vulnerable to shifts from snowfall to rainfall. This study evaluates the influence of future climate scenarios on the snowfall regime in the Sierra Nevada Mountains, an Alpine/Mediterranean climate region in southern Spain. Precipitation and temperature projections from two future climate scenarios representative concentration pathway (RCP) 4.5 and RCP 8.5, Fifth Assessment Report of the Intergovernmental Panel for Climate Change (AR5 IPCC)) were used to estimate the projected evolution of the snowfall regime on both annual and decadal scales during the period of 2006-2100. Specific snowfall descriptors of torrentiality are also analyzed. A general decrease of the annual snowfall was estimated, with a significant trend that ranged from 0.21 to 0.55 (mm•year −1)•year −1. These changes are dependent on the scenario and region in the study area. However, the major impact of future climate scenarios on the snowfall regime relates to an increased torrentiality of snowfall occurrence, with a decreased trend of the annual number of snowfall days (RCP 4.5: −0.068 (days•year −1)•year −1 and RCP 8.5: −0.111 (days•year −1)•year −1) and an increased trend in the annual mean snowfall intensity (RCP 4.5: 0.006 (mm•days −1)•year −1 and RCP8.5: 0.01 (mm•days −1)•year −1)) under both scenarios. This enhanced torrentiality is heterogeneously distributed, with the most semiarid region, which is currently the one least influenced by snow, being the region most affected within the study area.

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Section: Discussionsupporting

confidence: 69%

Climate Trends Impact on the Snowfall Regime in Mediterranean Mountain Areas: Future Scenario Assessment in Sierra Nevada (Spain)

Pérez-Palazón

Pimentel

Polo

2018

Water

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“…They noted decreasing and increasing trends in seasonal precipitation and temperature, respectively. In contrast, CORDEX‐SA‐projected changes in temperature over the Indian Himalayas showed statistically significant warming rates of 0.23°C decade −1 and 0.9°C decade −1 under the RCP 4.5 and 8.5 scenarios, respectively (Dimri et al ., ; ). To achieve high‐resolution projections for Italy, the EURO‐CORDEX model was used to analyse historical temperature and precipitation data (D'Oria et al ., ), revealing upward (+0.1°C decade −1 ) and downward trends (−22 mm decade −1 ), respectively.…”

Section: Introductionmentioning

confidence: 99%

Projected spatial patterns in precipitation and air temperature for China's northwest region derived from high‐resolution regional climate models

Yin

Feng

Deo

et al. 2019

Intl Journal of Climatology

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Derived from realistic global warming scenarios, long‐term projections of spatial patterns in precipitation and temperature in hydrology and climatology can serve to evaluate climate risk, explore sources of renewable energies and allow local‐scale data to inform decisions regarding agricultural, ecosystem, social, recreational and economic activities. Under the CORDEX‐EA project, the precipitation and temperature projections (2020–2045) for the economically and socially important region of Northwestern China were derived from high‐resolution regional climate model (RCM) simulations for RCP 4.5 and 8.5 scenarios, and compared against a historical period or baseline of 1980–2005. Drawing data from four key RCMs [Weather Research and Forecasting (WRF); Regional Spectral Model (RSM); Regional Climate Model version 4.0 (RegCM4); Mesoscale Model version 5 (MM5)], reliable local‐scale projections were generated by applying suitable bias correction that accords with the multivariate bias correction (MBC) approach. To validate this approach and then evaluate climate change impacts, the adjusted precipitation and temperature estimated from bias‐corrected models for the historical period were compared to the observed data. The results showed that the simulated spatiotemporal distribution of multiyear average precipitation and temperature appear to fit relatively well with the observations, however, the wet‐cold and dry‐warm climate‐related biases were still evident for the high altitude regions. Bias‐corrected future projections of RCMs indicated spatially averaged annual precipitation is expected to rise by about 23.6 and 35.3 mm under the RCP 4.5 and 8.5 scenarios, respectively, while spatially averaged annual temperature is expected to rise by about 1.95 and 1.10°C. Precipitation is projected to increase in all seasons, albeit, more so in the cold season (i.e., boreal winter and spring) than the warm season (i.e., boreal summer and autumn). The annual increase is expected to be about 56.7% under the RCP 4.5 scenario, compared to 67.6% under the RCP 8.5 scenario. The changes of mean temperature in winter are expected to be significant, −37.8% under both RCP scenarios. For spring, the mean temperature will rise by 23.1% (28.9%) under the RCP 4.5 (8.5) scenario. A MBC approach was found to be effective in yielding reliable projected changes in precipitation and temperature variables. The proposed approach has important implications for climatological studies and evaluation of climate change impacts on localized regions, not only in China, but also in other similar areas of the world, where decisions for managing climate risk must be implemented by policy makers, government, industry and stakeholders.

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“…One of such regional domains is South Asia (SA) which includes India and is the focus of present study. The CORDEX‐SA simulations without applying any bias correction are assessed in the past for seasonal mean precipitation and temperature changes over India and adjoining Himalayan region (Choudhary et al ., ; Ghimire et al ., ; Nengker et al ., ; Varikoden et al ., ; Choudhary and Dimri, ; ; Dimri et al ., ; ). However, the present study attempts to compare the skill of different bias correction methods in improving the results from CORDEX‐SA simulations in representing summer monsoon precipitation over India.…”

Section: Introductionmentioning

confidence: 99%

On bias correction of summer monsoon precipitation over India from CORDEX‐SA simulations

Choudhary

Dimri

2018

Intl Journal of Climatology

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In the present study, four different bias correction methods are assessed in improving the precipitation output from a set of two regional climate simulations for 1971–2005 period. The simulations come from Coordinated Regional Climate Downscaling Experiment over South Asia (CORDEX‐SA). The bias correction methods applied here are linear scaling (Scaling), empirical quantile mapping (EQM), power transformation (PTR) and local intensity scaling (LOCI). All the bias correction techniques show skill in improving the model results, but there are clear differences in the degrees of success depending upon the characteristic of precipitation studies—like seasonal mean, seasonal extremes or inter‐annual variability. Most of the methods show equivalently high ability in correcting the seasonal mean precipitation values; however, there also exist residual biases which vary spatially over India. The largest reduction in bias was observed for the frequency of dry days and precipitation on very wet days, which are significantly altered after adjustment by EQM and PTR. It is found that distribution‐based EQM method is more skilful followed by PTR and LOCI in most of the cases including correction of frequency‐based indices (e.g., dry days number and inter‐annual variability).

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Future changes over the Himalayas: Maximum and minimum temperature

Cited by 60 publications

References 110 publications

Climate Trends Impact on the Snowfall Regime in Mediterranean Mountain Areas: Future Scenario Assessment in Sierra Nevada (Spain)

Climate Trends Impact on the Snowfall Regime in Mediterranean Mountain Areas: Future Scenario Assessment in Sierra Nevada (Spain)

Projected spatial patterns in precipitation and air temperature for China's northwest region derived from high‐resolution regional climate models

On bias correction of summer monsoon precipitation over India from CORDEX‐SA simulations

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