Assessment of CORDEX-South Asia experiments for monsoonal precipitation over Himalayan region for future climate

Choudhary, Anubhav; Dimri, A. P.

doi:10.1007/s00382-017-3789-4

Cited by 46 publications

(23 citation statements)

References 95 publications

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“…Ghimire et al (2015) considering 11 CORDEX-SA RCMs, Sanjay et al (2017a) considering 10 RCMs, Sanjay et al (2017b) considering five RCMs and Mukherjee et al (2017) considering five RCMs show that most RCMs capture spatiotemporal pattern of South Asian precipitation, though skill in reproducing absolute observed values is variable. Nengker et al (2017) and Choudhary and Dimri (2018) considering five different RCMs find similar trends for temperature. Generally, the ensemble outperforms individual RCMs in hindcasting (Ghimire et al, 2015;Nengker et al, 2017).…”

Section: Introductionmentioning

confidence: 66%

“…5. For [2020–2099] relative to [1970–2005], ranges in colour maps in Figures and in (Choudhary and Dimri, ).…”

Section: Resultsmentioning

confidence: 99%

“…Generally, the ensemble outperforms individual RCMs in hindcasting (Ghimire et al, 2015;Nengker et al, 2017). However, studies highlight that biases in individual CORDEX-SA RCMs vary spatially (geographically and attitudinally) and temporally for both temperature and precipitation for both past (Ghimire et al, 2015;Nengker et al, 2017) and future climate (Choudhary and Dimri, 2018). Evaluation and correction of spatiotemporal biases is imperative for impact assessment studies, especially those focusing on hydrological application at finer scales, (Wilby, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Climate futures for Western Nepal based on regional climate models in the CORDEX‐SA

Dhaubanjar¹,

Pandey²,

Bharati³

2019

Intl Journal of Climatology

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With the objective to provide a basis for regional climate models (RCMs) selection and ensemble generation for climate impact assessments, we perform the first ever analysis of climate projections for Western Nepal from 19 RCMs in the Coordinated Regional Downscaling Experiment for South Asia (CORDEX‐SA). Using the climate futures (CF) framework, projected changes in annual total precipitation and average minimum/maximum temperature from the RCMs are classified into 18 CF matrices for two representative concentration pathways (RCPs: 4.5/8.5), three future time frames (2021–2045/2046–2070/2071–2095), three geographic regions (mountains/hills/plains) and three representative CF (low‐risk/consensus/high‐risk). Ten plausible CF scenario ensembles were identified to assess future water availability in Karnali basin, the headwaters of the Ganges. Comparison of projections for the three regions with literature shows that spatial disaggregation possible using RCMs is important, as local values are often higher with higher variability than values for South Asia. Characterization of future climate using raw and bias‐corrected data shows that RCM projections vary most between mountain and Tarai plains with increasing divergence for higher future and RCPs. Warmer temperatures, prolonged monsoon and sporadic rain events even in drier months are likely across all regions. Highest fluctuations in precipitation are projected for the hills and plains while highest changes in temperature are projected for the mountains. Trends in change in annual average discharge for the scenarios vary across the basin with both precipitation and temperature change influencing the hydrological cycle. CF matrices provide an accessible and simplified basis to systematically generate application‐specific plausible climate scenario ensembles from all available RCMs for a rigorous impact assessment.

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Section: Introductionmentioning

confidence: 66%

“…5. For [2020–2099] relative to [1970–2005], ranges in colour maps in Figures and in (Choudhary and Dimri, ).…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Climate futures for Western Nepal based on regional climate models in the CORDEX‐SA

Dhaubanjar¹,

Pandey²,

Bharati³

2019

Intl Journal of Climatology

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show abstract

“…Precipitation is a key climate variable in the global climate system and has an important impact on the hydrological cycle and the ecological system [7][8][9]. Scientific evidence has shown that precipitation patterns change in a warmer climate, and the likelihood of extreme precipitation is expected to increase due to global warming [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Performance of CMIP5 Global Climate Models for Simulating Future Precipitation Change in the Tibetan Plateau

Kang

Ruan

Yang

et al. 2019

Water

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In this study, the performance of 33 Coupled Model Intercomparison Project 5 (CMIP5) global climate models (GCMs) in simulating precipitation over the Tibetan Plateau (TP) was assessed using data from 1961 to 2005 by an improved score-based method, which adopts multiple criteria to achieve a comprehensive evaluation. The future precipitation change was also estimated based on the Delta method by selecting the submultiple model ensemble (SMME) in the near-term and far future (2051-2095) periods under Representative Concentration Pathways (RCP) scenarios RCP4.5 and RCP8.5. The results showed that most GCMs can reasonably simulate the precipitation pattern of an annual cycle; however, all GCMs overestimated the precipitation over TP, especially in spring and summer. The GCMs generally provide good simulations of the temporal characteristics of precipitation, while they did not perform as well in reproducing its spatial distributions. Different assessment criteria lead to inconsistent results; however, the improved rank score method, which adopts multiple criteria, provided a robust assessment of GCMs performance. The future annual precipitation was projected to increase by~6% in the near-term with respect to the period 1961-2005, whereas increases of 12.3% and 16.7% are expected in the far future under RCP4.5 and RCP8.5 scenarios, respectively. Similar spatial distributions of future precipitation changes can be seen in the near-term and far future periods under the two scenarios, and indicate that the most predominant increases occurred in the north of TP. The results of this study are expected to provide valuable information on climate change, and for water resources and agricultural management in TP.

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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, 2017;Ghimire et al, 2018;Nengker et al, 2018;Varikoden et al, 2018;Choudhary and Dimri, 2018a;Dimri et al, 2018a;. 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.…”

mentioning

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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Assessment of CORDEX-South Asia experiments for monsoonal precipitation over Himalayan region for future climate

Cited by 46 publications

References 95 publications

Climate futures for Western Nepal based on regional climate models in the CORDEX‐SA

Climate futures for Western Nepal based on regional climate models in the CORDEX‐SA

Assessing the Performance of CMIP5 Global Climate Models for Simulating Future Precipitation Change in the Tibetan Plateau

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

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