Impact of domain size on the simulation of Indian summer monsoon in RegCM4 using mixed convection scheme and driven by HadGEM2

Abstract

The original version of the article has been published with a sub title 'Impact of domain size on ISM simulations' printed just below the main title. It should be removed from the title page of the article. Further, the running head in the right hand pages should read as 'Impact of domain size on ISM simulations' instead of 'mixed convection scheme and driven by HadGEM2'. The online version of the original article can be found under

Assessment of two versions of regional climate model in simulating the Indian Summer Monsoon over South Asia CORDEX domain

“…The circulation and precipitation over Indian landmass have large changes at the time scales of month and season. The Regional Climate Model (RegCM, Giorgi et al 1993a, b) of the Abdus Salam International Centre for Theoretical Physics (ICTP) is one such regional model, which has been successfully used to study the Indian summer monsoon features (Dash et al 2006(Dash et al , 2013(Dash et al , 2015Singh and Oh 2007;Ashfaq et al 2009;Pattnayak et al 2013aPattnayak et al , b, 2016aDimri 2014, 2016). The experiments conducted by Giorgi and Marinucci (1996) with different topography indicated that resolution has a greater impact on the simulation through the model physics and dynamics rather than through the topography itself, especially for summer convection.…”

“…Ratnam et al (2009) coupled the RegCM3 with Regional Ocean Modeling System (ROMS) and showed that the coupled model simulates more realistic spatial and temporal distributions of monsoon rainfall compared to the uncoupled atmosphere-only model. Dash et al (2015) conducted domain size experiment by taking two different domains one over India and the other over South Asia using RegCM4. Their study suggests that South Asia domain is the better domain for studying the Indian summer monsoon using RegCM4.…”

“…Mostly, regional models are tuned and evaluated according to their ability to simulate temperature and precipitation (e.g., Giorgi et al 2012;Pattnayak et al 2013a;Maharana and Dimri 2014;Dash et al 2015) over a specific region. However, the role of other climatological parameters such as radiative and moisture fluxes should also be included in the evaluation procedure of regional models Alexandri et al 2015).…”

Climate Change Impacts During Near, Mid and End Century Over Rice Bowl of Tamil Nadu, India

“…In the case of rainfall, the model variability was high with lower correlation (0.32) and higher RMSE (172.51) and while analysing the data it was revealed that, GFDL models overestimated rainfall in this region whereas HadGEM2 underestimated rainfall. This result goes in tally with Dash et al, 2014 where he founded out HadGEM2 underestimated rainfall over most parts of India except Western Ghats and Arunachal Pradesh.…”

Performance of Multimodel Schemes for Seasonal Precipitation over Indian Region

“…An ETS of 0.3 is considered a good one in case of rainfall [38]. For the various categories of rainfall (light, moderate, and heavy rains) Dash et al [39] get ETS values of 0.24 to 0.03 over the Indian region. Next in case of BIAS (Figure 4(b)) BLUE shows least BIAS for positive thresholds while superensemble scheme shows least BIAS for negative thresholds.…”