Assessment of the performance of CORDEX-SA experiments in simulating seasonal mean temperature over the Himalayan region for the present climate: Part I

Abstract

a seasonal response of model performance and its spatial variability to elevation is found.

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

“…The bias is worst at mountain station 202, with biases as high as −29.5 C in the monsoon (JJAS), while performance is best at hill station 104. The observed cold bias is consistent with Nengker et al (2017)'s findings of seasonal biases of −7 C on average and as high as −14 C for the western HKH. However, these RCM temperature biases are higher compared to GCM biases of −6.0 to 2.5 C reported by Lutz et al (2016) for the entire HKH.…”

“…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).…”

“…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). 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).…”

“…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).…”

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

“…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.…”