Theoretical and Applied Climatology https://doi.org/10.1007/s00704-018-2432-6 ORIGINAL PAPER Aspect of ECMWF downscaled Regional Climate Modeling in simulating Indian summer monsoon rainfall and dependencies on lateral boundary conditions Soumik Ghosh 1 & R. Bhatla 1,2 & R. K. Mall 2,3 & Prashant K. Srivastava 2,3 & A. K. Sahai 4,5 Received: 28 February 2017 / Accepted: 19 February 2018 # Springer-Verlag GmbH Austria, part of Springer Nature 2018 Abstract Climate model faces considerable difficulties in simulating the rainfall characteristics of southwest summer monsoon. In this study, the dynamical downscaling of European Centre for Medium-Range Weather Forecast’s (ECMWF’s) ERA-Interim (EIN15) has been utilized for the simulation of Indian summer monsoon (ISM) through the Regional Climate Model version 4.3 (RegCM-4.3) over the South Asia Co-Ordinated Regional Climate Downscaling EXperiment (CORDEX) domain. The complexities of model simulation over a particular terrain are generally influenced by factors such as complex topography, coastal boundary, and lack of unbiased initial and lateral boundary conditions. In order to overcome some of these limitations, the RegCM-4.3 is employed for simulating the rainfall characteristics over the complex topographical conditions. For reliable rainfall simulation, implementations of numerous lower boundary conditions are forced in the RegCM-4.3 with specific horizontal grid resolution of 50 km over South Asia CORDEX domain. The analysis is considered for 30 years of climatological simulation of rainfall, outgoing longwave radiation (OLR), mean sea level pressure (MSLP), and wind with different vertical levels over the specified region. The dependency of model simulation with the forcing of EIN15 initial and lateral boundary conditions is used to understand the impact of simulated rainfall characteristics during different phases of summer monsoon. The results obtained from this study are used to evaluate the activity of initial conditions of zonal wind circulation speed, which causes an increase in the uncertainty of regional model output over the region under investigation. Further, the results showed that the EIN15 zonal wind circulation lacks sufficient speed over the specified region in a particular time, which was carried forward by the RegCM output and leads to a disrupted regional simulation in the climate model. Keywords ECMWF . Regional climate model . Dynamical downscaling . Summer monsoon . Boundary condition . CORDEX Key Points 1. Assessment of sensitivity of RegCM’s Mix99 (Grell -> Land & Emanuel -> Ocean) Convective Parameterization Scheme (CPS) in simulating Intraseasonal variability of Indian summer monsoon (ISM). 2. Evaluation of mixed CPS and its dependencies on boundary conditions. 3. Low efficiency of in situ lateral boundary conditions over the particular region causes a disturbed rainfall characteristic in RegCM simulation. 4. Low efficiency in EIN15 zonal wind circulation over Indian region during the phases of monsoon. * R. Bhatla rbhatla@bhu.ac.in 3 Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India 1 Department of Geophysics, Institute of Science, Banaras Hindu University, Varanasi, India 4 Indian Institute of Tropical Meteorology, Pune, Maharashtra, India 2 DST-Mahamana Centre of Excellence for Climate Change Research, Institute of Environment & Sustainable Development, Banaras Hindu University, Varanasi, India 5 India Meteorological Department, Pune, India S. Ghosh et al. Abbreviations BATS Biosphere-Atmosphere Transfer Scheme BoB Bay of Bengal CCSM3 Community Climate System Model version 3 CLM Climate Local Model CMO Conditional model output CORDEX Co-Ordinated Regional Climate Downscaling EXperiment COSMO Consortium for Small-Scale Modeling CPS Convective Parameterization Scheme ECDF Empirical cumulative distribution function ECHAM5 Fifth-generation atmospheric GCM developed at the Max Planck Institute for Meteorology ECMWF European Centre for Medium-Range Weather Forecasts EIN15 ERA-Interim ERSST Extended Reconstructed Sea Surface Temperature GCM Global climate model hPa Hectopascal ICBC Initial condition and boundary condition ICTP International Center for Theoretical Physics IMD India Meteorological Department ISM Indian summer monsoon ISMR Indian summer monsoon rainfall LLJ Low-level jet MM5 Mesoscale model version 5 MPIOM Max Planck Institute Ocean Model MSLP Mean sea level pressure NCAR National Center for Atmospheric Research NCDC National Climate Data Center NOAA National Oceanic and Atmospheric Administration OI_WK OISST in weekly pattern OISST Optimum Interpolation Sea Surface Temperature OLR Outgoing Longwave Radiation Q-Q Quantile–quantile RCM Regional Climate Model RegCM RCM by ICTP SD Standard deviation SST Sea Surface Temperature 1 Introduction One of the most important features of global atmospheric circulation is the Indian summer monsoon (ISM) (Webster et al. 1998). Any alteration in this circulation causes a direct impact on the developing country like India, where 60% of agriculture depends only on the seasonal monsoonal rainfall (Central Statistical Organization 1998). For understanding this, many studies have been conducted in the past and well documented in the technical literature domain by using the state of the art Regional Climate Model (RCM) (Bhatla et al. 2016; Singh et al. 2017) and Global Climate Model (GCM) (ParthSarthi et al. 2016, 2015). The RCM and GCM are the two widely used tools, which are generally used to simulate climate circulation processes and understanding the system. In recent decade, a number of studies have been conducted to simulate the ISM variability by using the RegCM (Bhatla et al. 2016; Maharana and Dimri 2016; Dash et al. 2015; Raju et al. 2015; Sinha et al. 2013; Maurya et al. 2017; Dobler and Ahrens 2010). However, the use of high-resolution RegCM in simulation of intraseasonal monsoon variability and its different epochs is still limited in the literature. In the last decade, several studies have been conducted with downscaled global reanalysis datasets from RegCM over particular boundaries (Bhatla et al. 2016; Dobler and Ahrens 2010; Saeed et al. 2009; Dash et al. 2015; VenkataRatnam and Kumar 2005; Park and Hong 2004). In the sensitivity analysis by Dash et al. (2006) and Bhatla and Ghosh (2015), the authors have found that the Grell CPS is performing better than the other CPSs in simulations of rainfall distribution by RegCM. Bhatla et al. (2016) have shown the performance of CPSs in the RegCM-4.3 over Indian land-sea continental margin and found that the Tiedtke CPS is highly efficient when compared with the in situ observations. Dobler and Ahrens (2010) have considered the RegCM simulations with the Consortium for Small-Scale Modeling (COSMO) CLM RegCM over South Asia by using the input data from ECHAM5–MPIOM and a 45-year re-analysis of European Centre for Medium-Range Weather Forecasts (ECMWF). The study showed that the mean variability of monsoon indices improves when simulated using the COSMO RegCM and compared against the driving field of ECHAM5–MPIOM. With the use of GCM downscaled output at regional level (Almazroui 2016, 2012; Saeed et al. 2009), Tugba et al. (2016) simulated the seasonal rainfall with the RegCM-4.3.5 over Central Asia. All the above studies showed a better performance of the RCM by capturing the regional phenomena, although it varies depending on the seasons, parameterization schemes along with some biases, and the role of initial condition. Singh et al. (2017) suggested a proper evaluation of RCM model output and its utilization in further climate studies by highlighting the poorer simulation of GCM downscaled output than the respective GCM simulations. According to them, most of RCMs have not added any significant output for the past, present, and future time scales in simulating Indian summer monsoon rainfall (ISMR) behavior as compared to the GCMs. Most of the studies have raised questions in RCM’s performance by considering various initial condition and boundary condition (ICBC) for climate modeling study (Mishra et al. 2014; Singh et al. 2017; Dash et al. 2015) rather than cross verifying the host GCM simulations and finding out the authenticity of the RCM’s ICBC. Aspect of ECMWF downscaled Regional Climate Modeling in simulating Indian summer monsoon rainfall and... In purview of the above, this study aims to improve the simulation of the dynamical mechanism of ISM by using the time-dependent ECMWF ICBC, through the RegCM-4.3 model downscaling along with the various sea surface temperature (SST) specified over the ocean. Further, through this study, an attempt has been made to understand the dependencies of model simulation with ERA-Interim (EIN15) ICBC and deducing its impact on the model simulated rainfall over a particular region during the phases of ISM rainfall. 2 Data and methodology 2.1 RegCM-4.3 outline The conceptual innovation of the RegCM was originally developed in the late 1980s from the National Center for Atmospheric Research (NCAR), USA. With the core of MM4 (Grell et al. 1994), Giorgi and Bates (1989) and Dickinson et al. (1989) introduced the first version of RegCM model in 1989. After several upgrades in model physics, RegCM-3 was introduced in which model grid spacing was extended between 10 and 100 km with simultaneous ranging from seasonal to centennial periods covering all land regions over the world (except Polar region). This version of RegCM was more portable and developed with an aim to simulate the tropical climates (Giorgi and Anyah 2012). The RegCM-4 is then upgraded with integration of new land surface physics, planetary boundary layer conditions and air–sea flux scheme. With the modification in pre-existing radiative transfer and boundary layer schemes, the mixed convection scheme was developed and the model code was upgraded to improve its flexibility and applicability (Giorgi et al. 2012). The RegCM-4.3 is a hydrostatic model with sigma-p vertical coordinate system and having capability to run over a large range of Regional Climate Modeling system (Giorgi et al. 2012). It is the first limited area model with the mesoscale model version 5 (MM5) dynamical core, developed by the International Center for Theoretical Physics (ICTP) for a long-range climate simulation running on Arakawa B-grid. In this version, two types of land-use (BATS) have been added for the better representation of the urban and suburban environments. For urban development, the surface albedo has been modified and the surface energy balance is modified for alter. The Climate Local Model (CLM) version 3.5 is coupled with the RegCM-4.3, which deals with the bio-geophysical-based parameterization and to describe the exchanges of energy, momentum, water, and carbon (Tawfik and Steiner 2011) along with the physical process given by Holtslag et al. (1990) for the planetary boundary layer (PBL). The RegCM-4.3 has four core CPSs: Grell (Grell 1993), Emanuel (Emanuel 1991; Emanuel and Živković-Rothman 1999), Tiedtke, and Kuo. The Arakawa–Schubert or the Fritsch and Chappell (1980a, 1980b) type closures are available to use with the Grell CPS. It has the capacity to perform combinations of different CPSs over the land and ocean. The RegCM-4.3 has been forced to simulate the mixed convection scheme mode: Mix98 (Grell over the ocean, Emanuel over the land) and Mix99 (Grell over the land, Emanuel over the ocean). The lower boundary conditions are derived from six hourly forcing of EIN15 re-analysis. These ICBCs are obtained with the 1.5o horizontal grids and 37 vertical levels, which incorporate the CCSM3 radiation scheme of NCAR Community (Kiehl et al. 1996; Collins et al. 2006). The CPSs are forced with the Optimum Interpolation Sea Surface Temperature (OISST) in weekly pattern (OI_WK) and Extended Reconstructed Sea Surface Temperature (ERSST) wherever applicable. The dataset of OI_WK at weekly and ERSST at six hourly timescales are obtained from the National Oceanic and Atmospheric Administration (NOAA) and National Climate Data Center (NCDC), respectively. The details of the RegCM-4.3 configuration are listed in Table 1. 2.2 Experiment design The complicated characteristic of South Asian summer monsoon rainfall faces considerable difficulty in rainfall simulation over the region (Webster et al. 1998). Dealing with the complex topography and coastline terrain, the RegCM is equipped with advance numerical and physical schemes (Giorgi et al. 2001). With the flexibility in choosing the appropriate CPS over a particular domain, this study is continued with Mix99 CPS. Several studies have showed the better performance of Grell CPS over the land region (Bhatla and Ghosh 2015; Elguindi et al. 2013) and produced good simulation of Emanuel CPS over the ocean. The combination of core CPSs (Grell and Emanuel) with their better side is termed as Mix99 scheme, where Grell CPS is used over the Land and Emanuel over the ocean. The variation in mean seasonal rainfall has a strong association with the global phenomena through the influence of SST (Krishnamurthy and Kinter 2003). Therefore, for sensitivity analysis, the conditional model outputs (CMOs) are continued with two types SST (ERSST and OI_WK) as lower boundary conditions. For better representation in response to climate dynamics, associations with atmospheric convection and topographical complexity have been conducted at 0.5o × 0.5o fine resolution through the model simulation. For this synoptic study, the model simulated daily rainfall, Outgoing Longwave Radiation (OLR), mean sea level pressure (MSLP) and wind at 925 hPa and 850 hPa vertical pressure intervals over the South Asia CORDEX region (22oS-50oN and 10°E-130°E). The actual onset dates (Table 2) are considered as per the India Meteorological Department (IMD) guidelines, while the active/break spells are obtained from the NCC Research Report (2013) provided by the IMD (Table 3). 30 years S. Ghosh et al. Table 1 Model configuration of the RegCM4.3 Dynamics Hydrostatics Model domain South Asia CORDEX domain Domain cartographic projection Resolution Vertical level (15oS–45oN; 10°E–130oE) ROTMER––rotated Mercator 50 km horizontal 18 sigma vertical levels Initial and boundary conditions ERA15 SST 1. OI_WK – OISST weekly optimal interpolation dataset Land surface parameterization Radiation Parameterization PBL 2. ERSST – ERA-Interim 6 hourly 1.5°×1.5° SST Modified CCM3 Modified Holtslag Convective parameterization Mix99 (Emanuel over the ocean and Grell over the land) Grell closure scheme Arakawa and Schubert (1974) (1981–2010) climatological composites of the considered parameters are analyzed to find out the predictive skills. Maharana and Dimri (2016) have illustrated the simulation process and the physics of the RegCM, which plays a dominant role in defining the intraseasonal variability. But the model simulation is not well correlated with the in situ observations obtained during the intraseasonal/interannual rainfall simulations, and the correlation coefficient is found close to zero (Maharana and Dimri 2014, 2016). Therefore, the correlation coefficient has not been considered as a verification tool for different CMOs with the in situ observations. In this study, the performance verification between in situ and the model derived outputs is verified using quantile–quantile (Q-Q) distribution, empirical cumulative distribution function (ECDF), SD, and absolute bias (bias) for different phases of summer monsoon. The bias can be measured as follows: h i Bias ¼ y−x where the optimal value is 0 and the low magnitude value indicates high accuracy in model simulation. x represents the mean of the observed/reanalyzed data and y is the mean of the model output. 3 Results and discussion 3.1 Variability of summer monsoon with different lower boundary conditions 3.1.1 Onset phase In order to simulate the summer monsoon rainfall variability, the ICBC ERSST and OI_WK SST forcing are considered. Spatial distributions of CMOs and in situ datasets for different phases of summer monsoon are provided in Figs. 1 to 3. Due Table 2 Actual and model simulation onset dates along with their deviation from actual onset of ISM with different initial condition Onset dates Year 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Actual 30 M 28 M 13 J 30 M 28 M 04 J 02 J 26 M 03 J 19 M 02 J 05 J 28 M 28 M 05 J 03 J 09 J 02 J 25 M 01 J 23 M 29 M 08 J 18 M 05 J 26 M 28 M 31 M 23 M 31 M Where M May and J June OI_WK – 04 J (+ 7) 21 J (+ 8) 31 M (+ 1) 29 M (+ 1) 25 M (− 10) 08 J (+ 6) 23 M (− 3) 25 M (− 9) 19 M (0) 07 J (+ 5) 06 J (+ 1) 30 M (+ 2) 06 J (+ 9) 11 J (+ 6) 24 M (− 10) – 30 M (− 3) 02 J (+ 8) 25 M (− 7) 26 M (+ 3) 17 M (− 12) 06 J (− 2) 21 M (+ 3) 07 J (+ 2) 25 M (− 1) – 31 M (0) – 19 M (− 12) ERSST 24 M (− 6) 02 J (+ 5) 21 J (+ 8) 31 M (+ 1) 06 J (+ 9) 24 M (− 11) 07 J (+ 5) 27 M (+ 1) 29 M (− 5) 18 M (− 1) 02 J (0) 25 M (− 11) 31 M (+ 3) 04 J (+ 7) 11 J (+ 6) 03 J (0) 29 M (− 11) 30 M (− 3) 21 M (− 4) – 22 M (− 1) 17 M (− 12) – 12 M (− 6) 09 J (+ 4) 28 M (+ 2) 01 J (+ 4) 26 M (− 5) – 21 M (− 10) Aspect of ECMWF downscaled Regional Climate Modeling in simulating Indian summer monsoon rainfall and... Table 3 Active/break periods and days of summer monsoon during 1981–2010 Year Active spells Break spells No. of active/break spells No. of active/break days 1981 1982 7–10 J 24–27 A Act = 1, brk = 1 Act = 5, brk = 4 21–23 A, 12–14 A 1–8 J Act = 2, brk = 1 Act = 6, brk = 8 1983 1984 1985 18–20 A 1–3 A, 9–11 A, 16–19 A 15–17 J, 30 J–2 A, 6–8 A 14–16 J 28–30 J 23–25 A Act = 1, brk = 1 Act = 3, brk = 1 Act = 3, brk = 1 Act = 3, brk = 3 Act = 10, brk = 3 Act = 10, brk = 3 1986 21–24 J, 13–15 A 23–31 A Act = 2, brk = 1 Act = 7, brk = 9 1987 1988 24–29 A 26–28 J 23–25 J, 30 J–4 A, 9–13 A 14–17 A Act = 1, brk = 3 Act = 1, brk = 1 Act = 6, brk = 14 Act = 3, brk = 4 1989 1990 – 2–4 J, 21–24 A, 29–31 A 18–20 J, 30 J–3 A – Act = 0, brk = 2 Act = 3, brk = 0 Act = 0, brk = 8 Act = 10, brk = 0 1991 1992 21–24 J, 29–31 J, 22–24 A 27–29 J, 16–21 A 1-3 J 4–10 J Act = 3, brk = 1 Act = 2, brk = 1 Act = 10, brk = 3 Act = 9, brk = 7 1993 7–11 J, 15–17 J 2–4 J, 9–16 J, 30 J–2 A, 18–20 A, 25–27 A, 29–31 A 18–20 J, 22–25 J 24–28 J, 19–21 A 30 J–1 A, 21–25 A 30 J–1 A, 21–25 A 7–9 A 12–14 J, 17–20 J 9–12 J 23–25 A 23–28 J, 27–29 A 8–13 A, 21–23 A 1-5 J, 25–29 J, 31 J–2 A 3–6 J, 27 J–7 A, 13–15 A, 17–20 A 1–9 J, 6–9 A 10–12 A 20–23 J, 8–13 A, 22–28 A – Act = 2, brk = 3 Act = 6, brk = 0 Act = 8, brk = 17 Act = 24, brk = 0 3–7 J, 11–17 A 11–13 A 11–15 J, 9–17 A 21–26 J, 16–21 A 1–5 J, 12–18 A, 22–24 A 29 J–8 A 31 J–2 A, 27–29 A 4–16 J, 22–31 J – 10–13 J, 19–22 J, 26–31 A 7–14 A, 24–31 A – 18–23 J, 15–17 A 16–21 J, 21–24 A, 28–30 A Act = 2, brk = 2 Act = 2, brk = 1 Act = 2, brk = 2 Act = 2, brk = 2 Act = 1, brk = 3 Act = 2, brk = 1 Act = 1, brk = 2 Act = 1, brk = 2 Act = 2, brk = 0 Act = 2, brk = 3 Act = 3, brk = 2 Act = 4, brk = 0 Act = 2, brk = 2 Act = 1, brk = 3 Act = 7, brk = 12 Act = 8, brk = 3 Act = 8, brk = 14 Act = 8, brk = 12 Act = 3, brk = 15 Act = 7, brk = 11 Act = 4, brk = 6 Act = 3,brk = 23 Act = 9, brk = 0 Act = 9, brk = 14 Act = 13, brk = 16 Act = 23, brk = 0 Act = 13, brk = 9 Act = 3, brk = 13 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 13–16 J, 20–23 J 2010 – Total (1981–2010) No. of spells: Act = 59, brk = 44 29 J–9 A, 17–19 A Act = 2, brk = 2 – Act = 0, brk = 0 No. of days: Act = 237, brk = 246 Act = 8, brk = 15 Act = 0, brk = 0 J July, A August, act active, brk break to unavailability of regular time period of SST, the data length for in situ and CMO ERSST are considered only for the period 1981–2010, while for CMO OI_WK runs are provided for the time period 1982–2010. The rainfall and OLR distribution during the onset phase of summer monsoon rainfall are represented through Fig. 1a–f. For simulating the onset phase, IMD onset simulation criteria (Pai and Rajeevan 2007) are considered. The onset criterion is emphasized by using the peak of rainfall over Kerala with four (4) newly developed conditions, which is dependent upon the three climatic parameters. Sixty percent of rainfall stations is found in the Kerala (a total of 14 stations namely Minicoy, Amini, Thiruvananthapuram, Punalur, Kollam, Allapuzha, Kottayam, Kochi, Trissur, Kozhikode, Talassery, Cannur, Kasargode, and Mangalore) with a minimum rainfall of 2.5 mm/day for the two consecutive days (after 10th of May) which is considered as the onset of monsoon. For declaration of onset, another three criteria also have to be satisfied along with the station wise rainfall criteria. The depth of westerly will have to be maintained at 600 hPa over 0oN–10oN and 55°E–80°E regions. Zonal wind will have to be blown at a speed of 15–20 knots at 925 hPa over the Lat 5oN–10oN and Lon 70°E–80°E, and INSAT OLR should be less than 200 Wm−2 over Lat 5oN– 10oN and Lon 70°E–75°E. High-resolution (0.25 × 0.25o) IMD rainfall data is considered for spatial and temporal rainfall analysis which is distributed only over the Indian subcontinent. Two stations (Minicoy and Amini) among the 14 stations do not have the rainfall distributed area in IMD rainfall data. Therefore, only 12 stations are considered to carry out this present study. Table 2 S. Ghosh et al. Fig. 1 a–f Spatial pattern of in situ and CMO-simulated rainfall and OLR distribution during 1981–2010 by following IMD criteria for declaring monsoon onset illustrates the actual onset along with CMO onset simulation followed by IMD criteria. Some years are left blank that do not satisfy the said criteria. IMD mean rainfall distribution (Fig. 1a) during the two consecutive days (previous day of actual onset and the actual onset) showed a densely rainfall distribution over the adjoining part of the Kerala and Western Ghat region and represents 2.5 mm/day of rainfall criteria over the Kerala region. The ERSST CMO (Fig. 1b) and OI_WK Fig. 2 a–d Distribution of in situ and CMOs simulation of OLR over the region Lat 5°N–10°N and Lon 70°E–75°E and zonal wind at 925 hPa over the region Lat 5°N–10°N and Lon 70°E– 80°E for the respective onset date CMO (Fig. 1c) are following the pattern of IMD rainfall distribution during two consecutive days of model-simulated onset. Because of unavailability of the long-range climatological INSAT OLR, in this study, the CMO OLR distribution is cross-verified by using the NOAA in situ data instead of INSAT OLR. Figure 1d represents the NOAA in situ OLR distribution where CMO ERSST and CMO OI_WK are considered and shown in Fig. 1e, f, respectively. Distributions of Aspect of ECMWF downscaled Regional Climate Modeling in simulating Indian summer monsoon rainfall and... Fig. 3 a–i Composite spatial pattern of in situ and CMOsimulated rainfall, OLR, and MSLP distribution during active phases for duration 1981–2010. EIN15 and CMO-derived wind circulation is superimposed at 850 hPa for respective cases CMO OLR pattern are in close match with the in situ OLR distribution and correctly following the in situ OLR distribution over Lat 5oN–10oN and Lon 70°E–75°E and satisfying the OLR distribution of less than 200 Wm−2 criteria. The temporal distributions of OLR over the region are also considered in Fig. 2a, which represent warm bias for some cases of NOAA OLR on actual onset date. But declaring onset over Indian region, NOAA as well as CMOs should maintain the limit of less than 200 Wm−2 over the respective region (IMD criteria) on the onset day. In this figure, the dotted line is indicating the limit band at 200 Wm−2. But with a large variation, in situ OLR distribution (Fig. 2b) is found in between ~ 160–190 Wm−2, while the 25th and 75th percentiles and its median are found towards the lower head of the inter quartile range box. Therefore, the spatial pattern of in situ OLR (Fig. 1d) represents the best fit with the IMD onset criteria. CMO ERSST and OI_WK are showing some deviations in the simulated onset dates, as shown in the box plots (Fig. 2b). Zonal wind speed at 925 hPa over Lat 5oN–10oN and Lon 70°E–80°E is considered in Fig. 2c in which red dotted line represents the upper limit band (20 knots) and blue dotted line indicates the lower limit band (15 knots). In Fig. 2c, EIN15 in situ zonal wind speed is showing large variations in the actual onset date during some of the years. More than 36% in situ zonal wind dataset is not satisfying the IMD onset zonal wind speed criteria. Therefore, over a specified region, a large variation is being observed in EIN15 dataset from its normal wind speed (Srivastava et al. 2013) on the actual onset date. The simulations using CMO ERSST and OI_WK (Fig. 2c, d) are kept within the limit band during the analysis, which causes a mean deviation of ± 5 day from the actual onset for a 30-year time period (from Table 2). S. Ghosh et al. A relation between OLR and zonal wind speed is depicted in Fig. 2, which represents a negative correlation between wind speed and radiation (Bett and Thornton 2016) where the OLR value (Fig. 2a) is reduced with the increase in zonal wind speed (Fig. 2c). This feature is also well simulated in the CMO ERSST as well as CMO OI_WK simulation. The depth of westerly of in situ, CMO ERSST, and CMO OI_WK are maintained at 600 hPa over 0oN–10oN and 55°E–80°E region during the last few days of actual onset (figures not shown). Using the said criteria and by considering the above analysis, we have tried to simulate the onset date for Indian region for a 30-year time period (Table 2), which shows a mean deviation of ± 5 day and a maximum of ± 12 day deviation in CMOs (ERSST and OI_WK) from the actual date of onset. With a robust skill in capturing the intraseasonal monsoon variability, the RegCM has the limitation to simulate the dates in correspondence to in situ observations (Maharana and Dimri 2014, 2016). With the MM5 dynamical core, the RegCM-4.3 has performed moderately in simulation of onset dates and it might be due to the large variation in ICBC over the particular region. Therefore, in this study, we particularly have focused on the physical processes of the data distribution instead to find out the physics behind the uncertainty of the RegCM4.3 model output. 3.1.2 Active/break phase To evaluate the model performance in intraseasonal time scale and to analyze the RegCM-4.3 sensitivity with EIN15 Fig. 4 a–d Distribution of rainfall and OLR during active spells of 1981–2010 downscaled model output with two types of SST, this study has been carried out for another two major phases (active and break) of summer monsoon (Krishnamurti 1985; Krishnamurti and Ardunay 1980). The core region is considered between 71°E–83°E and 21oN–28oN which is closer to the monsoon region of Rajeevan et al. (2010). Active and break periods are considered from the NCC Research Report (2013) as given in (Table 3), which has been identified by standardized rainfall anomaly of greater than + 1 and less than − 1 for minimum three consecutive days respectively. The above criteria were obtained by averaging the daily rainfall over the core monsoon region and by standardizing the daily rainfall data by subtracting from its long-term normal and dividing by its daily SD (Rajeevan et al. 2010). A total of 59 active spells, 44 break spells along with 237 active days, and 246 break days are considered for 30 years during the period 1981–2010 (Table 3). The simulation of intraseasonal monsoon variability during active phases is considered in Figs. 3 and 4. MSLP has an important role for active and break phases (Krishnamurti and Bhalme 1976) and hence considered for further study. The composite analysis of active days for synoptic patterns is spatially distributed in Fig. 3a–i, in which wind circulation at 850 hPa is superimposed on rainfall (Fig. 3a–c), OLR (Fig. 3d–f), and MSLP (Fig. 3 g–i) distributions. Strong monsoon rainfall distribution (Fig. 3a–c) along with the low-level jet (LLJ) over the core region during the active phase is depicted with the CMO ERSST (Fig. 3b) and CMO OI_WK (Fig. 3c), which are closely following the IMD spatial rainfall distribution (Fig. 3a) and temporal pattern (Fig. 4a) during the phases. Aspect of ECMWF downscaled Regional Climate Modeling in simulating Indian summer monsoon rainfall and... Fig. 5 a–i Composite spatial pattern of in situ and CMOsimulated rainfall, OLR, and MSLP distribution during break phases for duration 1981–2010. EIN15 and CMO-derived wind circulation is superimposed at 850 hPa for respective cases Temporal pattern of CMO rainfall distribution (Fig. 4a–b) is showing a dry bias during this phase. The IMD rainfall (Fig. 4a; blue line) and its distribution (Fig. 4b) are spreaded between 12 to 19 mm of the rainfall amount where CMO ERSST (Fig. 4a; red line) and OI_WK (Fig. 4a; green line) are showing under estimation with 4 to 7 mm (Fig. 4b) of rainfall during the active days. During summer monsoon period, convection plays an important role in forming the cloud, which is inversely correlated with the OLR. Positive/negative anomalies in OLR distribution are the causes of negative/positive rainfall anomaly (Raju et al. 2009). The shrinking/rising distribution of in situ OLR (Fig. 3d) over core region is showing less than 200 Wm−2 during the active phase, which is poorly simulated by CMO ERSST (Fig. 3e) and CMO OI_WK (Fig. 3f) OLR distribution. The temporal pattern of CMO OLR (Fig. 4c) is showing warm bias, and its distributions (Fig. 4d) are varying up to 260 Wm−2, whereas NOAA represents 200 Wm−2 over the region. ISMR and OLR relationship has been already established in previous study by Raju et al. (2009) and indicated an opposite relationship between ISMR and OLR. In the current study, the CMO OLR distribution are showing warm biases and at the same time, the model-simulated rainfall distribution is representing dry biases. Simultaneously, a pressure belt (Fig. 3) is showing an average pressure of 995–1005 hPa over the Monsoon Convergence Zone (MCZ), and the monsoon trough covers the Gangetic region as a proxy of rainfall distribution and spreads up to foot of the Bay of Bengal (BoB) through the Gangetic plain. Similar results are also provided in the IMD Monsoon Report 2013, which is in agreement with the model-simulated MSLP (Fig. 3 h– S. Ghosh et al. Fig. 6 a–d Distribution of rainfall and OLR during break spells of 1981–2010 Fig. 7 a–d Vertical level-days (a, b) and days-longitude at 850 hPa (c, d) of EIN15 zonal wind distribution (Knots/s; shaded) during active and break phase over the core region (71oE-83oE and 21oN-28oN) Aspect of ECMWF downscaled Regional Climate Modeling in simulating Indian summer monsoon rainfall and... Fig. 8 Distribution of EIN15 zonal wind during active and break phases of Intraseasonal summer monsoon period i) as compared to NCEP (Fig. 3 g). In in situ MSLP distribution (Fig. 3 g), a low pressure belt of 995–1000 hPa is formed over the MCR and this gradient is not well simulated by the CMOs (Fig. 3 h–i). Due to topographical terrain over the Himalayan region, a deep low-pressure (< 990 hPa) belt is formed in the CMO MSLP (Fig. 3 h–i) and the model is failed to simulate the MSLP over the respective zone. As a proxy parameter in the RegCM rainfall simulation, MSLP does not have a big role in CMO over the MCR. Therefore, non-simulating OLR distribution could be a possible cause behind the agitated rainfall simulation during the active phase. Composite distribution of synoptic parameters during break phases is spatially distributed in Fig. 5a–i. These figures illustrate a weak monsoon rainfall over the core region, which is depicted well in IMD rainfall distribution (Fig. 5a). CMO ERSST and OI_WK are able to simulate this distribution over the core region (Fig. 5b, c). The temporal rainfall distributions during break phase are considered in Fig. 6a, which indicates that the CMOs are following the rainfall distribution pattern as IMD. Although, the rainfall deviation from the mean is slide high for CMO ERSST and OI_WK rainfall as compared to IMD rainfall (Fig. 6b). The less OLR distributed area is shifted towards the eastern part from the core region of India (Fig. 5d–f) Table 4 3.2 Dependencies of RegCM-4.3 output on lateral boundary conditions The above study shows that the sensitivity of model-simulated rainfall, OLR, MSLP, and zonal wind distributions over Indian subcontinent is showing an over or under estimation in simulating different phases of ISMR. Lateral boundary conditions of u and v wind components, surface pressure, temperature, and water vapor along with SSTs specified over the ocean are the regulators and are used to run the RegCM-4.3 over the specified region. The study of Bett and Thornton (2016) has shown the relationship of these parameters and responses to rainfall simulation. Zonal wind disturbances have been identified as the main mechanism in organizing rainfall pattern over a particular region (Diedhiou et al. 1999). Therefore, to find out possible causes behind the uncertain behavior of Statistical scores of the RegCM-4.3 simulation with in situ during the phases of monsoon Onset phase In situ ERSST OI_WK and found more than 220 Wm−2 over the Indian core region during this phase (Raju et al. 2009). Temporal distributions of the CMO analyses are also capturing the peak as NOAA OLR (Fig. 6c) with a slight over estimation in respect to in situ (Fig. 6d). At the same time, the monsoon trough in Fig. 5 g–i has been shifted towards the Himalayan foothills from the Gangetic plain and has extinct from BoB foot (Bhatla and Ghosh 2015). During the break phase, the pressure belt (995–1005 hPa) generally remains active and spreads over the Gangetic plain during the active period and shifted to the Himalayan foothill in in situ (Fig. 5 g) as well as CMOs (Fig. 5 h–i) and monsoon, while become weaker over the core region. These are the prominent feature during the break phases of ISMR and well simulated by different CMOs of Mix99 CPSs. The spatial gradient is also well simulated by CMOs over all the remaining part including BoB, Arabian Sea, and Indian Ocean (except the Himalayan foothill region) during active/break phases. Another major dependency of model performance is the ICBC. Therefore, to find out the possible causes behind disturbed rainfall and non-simulating the OLR distribution, the role of the RegCM’s ICBC for longterm simulation is carried out in further section. OLR SD 29.24 12.26 11.68 BIAS − 2.77 − 3.2 Active phase U-wind SD 4.46 1.32 1.87 BIAS − 1.54 − 1.08 Rainfall SD BIAS 5.06 2.84 − 9.8 2.71 − 9.5 Break phase OLR SD 20.02 8.56 9.22 BIAS 55.68 55.82 Rainfall SD BIAS 1.65 2.25 − 1.60 2.76 − 2.04 OLR SD 21.50 13.16 13.18 BIAS − 22.28 − 21.8 S. Ghosh et al. Mix99 CPS, we focused on the ICBC (specially zonal wind) provided for model run. Figure 7a–d represents Hovmöller diagram of vertical level wind structure-days Fig. 9 a–f Q-Q distribution of different synoptic parameters during onset, active, and break phase in respect to in situ dataset with 5% significant level (Fig. 7a–b) and days-longitude-wise distribution (Fig. 7c– d) of EIN15 zonal wind at 850 hPa during active and break phases over the Indian core region. The shaded area Aspect of ECMWF downscaled Regional Climate Modeling in simulating Indian summer monsoon rainfall and... represents the zonal wind speed in knots/s. This distribution illustrates the low efficiency of zonal wind distribution with the color shaded of 0–15 knots/s during active phase (Fig. 7a) and 10–20 knots/s on vertical level 900– 800 hPa during break phase (Fig. 7b). Generally, LLJ usually blows over the Indian subcontinent with a higher speed during the active condition than the weak and normal monsoon conditions (Ruchith et al. 2014). But EIN15 zonal speed circulation speed never achieved a value over 25 knots/s at the lower level. At 850 hPa, most of the active days attained wind speed of 0–10 knots/s and very few cases are there that achieved more than 15 knots/s during the active days (Fig. 7c). During the break conditions, the wind speed has shown an average of 10–25 knots/s (Fig. 7d) over the region. The study by Ruchith et al. (2014) and Varikoden (2006) showed that during active phase, the zonal wind blows with a higher speed over the core region rather than the normal and weak monsoon condition. Zonal wind blows from the Arabian Sea with the core speed of ~ 34 knots/s and crosses through the central India (core region) with average speed of ~ 28 knots/s at 850 hPa during active phase. During break phase, the direction of LLJ turns towards south and becomes weak and blows with a speed of less than ~ 15 knots/s (Varikoden 2006). In Fig. 8, box plot has been considered for clear representation of in situ zonal wind distribution which represents the EIN15 zonal wind distribution during active and break days with 25 and 75 percentile over the region. With a mean of 4 knots/s, in situ EIN15 zonal wind is blowing over the core region during active phase and in break phase, the mean speed is elevated up to 14 knots/s. Therefore, a sharp conclusion of the above figure might be drawn that the in situ zonal wind speed is showing a large under estimation in speed during active phase, whereas a higher speed is expected during the active phase. The dynamics of zonal wind circulation pattern during active and break phases of summer monsoon showed a large bias (~ 24 knots/s) in in situ zonal wind speed at 850 hPa during the active phase. During the break phase, the EIN15 zonal wind speed follows its regular condition (speed of less than ~ 15 knots/s) and blows with the speed ~ 14 knots/s. Rainfall is the most chaotic factor depends upon a combination of five environmental factors, and zonal wind is one of them. The model-simulated rainfall distribution purely depends on the ICBC in situ components. Therefore, a large bias in in situ distribution will have a direct impact in the model-simulated rainfall distribution. In this study, the lack/excess of zonal wind circulation speed during the ISM season is carried forward during model simulation process. Due to the negative correlation between the zonal wind speed and OLR distribution, the in situ zonal wind distribution forces the CMO OLR simulation. At the same time, the disturbance in OLR distribution affects the excess/lack of rainfall distribution over the rain belt area (Diedhiou et al. 1999) in the RegCM-4.3 simulation during the intraseasonal monsoon season (Sylla et al. 2010, 2011). 3.3 Statistical score and model verification 3.3.1 Statistical scores and biases Table 4 represents the statistical scores of modelsimulated output and in situ data during different phases of monsoon. Statistical representation of CMOs have showed less than half of SD in ERSST, OI_WK CMO OLR, and U-wind distribution with respect to the in situ during onset phase and the bias over the respective region, which represents dry for the parameters of OLR and zonal wind. Previous analyses (Section 4.1) showed a high fluctuation in in situ over the respective region, and the mean for a 30-year period has induced the analyses to disfavor the CMO performance. In this study, onset simulations by CMOs are considered following the IMD criteria; therefore, the biases are unexpected in model simulation, which lay the overestimation in in situ datasets. During active/break phases, the SD in CMO rainfall and OLR is depicted roughly a half of in situ, while biases represent the opposite relationship among rainfall and OLR distribution with high positive biases in OLR as stated in the above section. The bias in OLR distribution over the core region is higher during active period than break period, which attains about double in respect to break phase. Analyses from Section 4.1 and Section 4.2 showed the mechanism of ICBC in the RegCM and impact of wind circulation on OLR as well as rainfall distribution, and a relationship between zonal wind, OLR, and rainfall is also evident in the observed datasets. Those sections particularly dealing with the uncertainties in the RegCM rainfall simulation due to disturbed ICBC over some specified region during different phases of ISM, where this section supporting the analyses and highlighting the nature of CMOs by the statistical score rather than verifying the model with the in situ itself. 3.3.2 Model verification Verification statistics of CMOs are considered using Q-Q distribution and ECDF for the considered parameters for the synoptic analysis during the phases of ISMR. Q-Q plot is a probability plot to compare the shapes of d i s t r ib u t i o n b e t w e e n t w o d a t a s e r i e s ( Wi l k a n d Gnanadesikan 1968) or for its theoretical distribution itself S. Ghosh et al. (a) Onset: Q-Q Plot of EIN15 wind 30 (b) Onset: Detrended Q-Q Plot of EIN15-wind 3 2 Deviation from Normal Expected Normal Value 25 20 15 1 0 -1 10 -2 5 -3 5 10 15 20 25 30 5 10 15 Observed Value (c) Onset: Q-Q Plot of Mix99 wind (ERSST) 20 20 25 30 Observed Value 1.5 (d) Onset: Detrended Q-Q Plot of Mix99 wind (ERSST) 1.0 18 Deviation from Normal Expected Normal Value 19 17 16 0.5 15 0.0 14 13 -0.5 13 14 15 16 17 18 19 20 14 15 16 (e) Onset: Q-Q Plot of Mix99 wind (OI_WK) 22 18 19 20 (f) Onset: Detrended Q-Q Plot of Mix99 wind (OI_WK) 1.5 20 1.0 Deviation from Normal Expected Normal Value 17 Observed Value Observed Value 18 16 14 0.5 0.0 -0.5 12 -1.0 12 14 16 18 Observed Value 20 22 14 15 16 17 18 19 20 21 Observed Value Fig. 10 a–l Q-Q distribution of the individual datasets with 1% significant band along with the detrended Q-Q distribution of the respective dataset considered for onset simulation Aspect of ECMWF downscaled Regional Climate Modeling in simulating Indian summer monsoon rainfall and... 240 (g) Onset: Q-Q Plot of NOAA-olr (h) Onset: Detrended Q-Q Plot of NOAA-olr 30 220 200 Deviation from Normal Expected Normal Value 20 180 160 10 0 140 -10 120 100 100 150 200 -20 140 250 160 200 220 240 260 Observed Value Observed Value 210 180 (i) Onset: Q-Q Plot of Mix99 olr (ERSST) 10 (j) Onset: Detrended Q-Q Plot of Mix99 olr (ERSST) Deviation from Normal Expected Normal Value 200 190 180 170 5 0 160 150 150 160 170 180 190 200 -5 160 210 170 210 180 190 200 210 Observed Value Observed Value (k) Onset: Q-Q Plot of Mix99 olr (OI_WK) 5.0 (l) Onset: Detrended Q-Q Plot of Mix99 olr (OI_WK) 200 Deviation from Normal Expected Normal Value 2.5 190 180 170 0.0 -2.5 -5.0 160 150 150 160 170 180 Observed Value Fig. 10 (continued) 190 200 210 -7.5 150 160 170 180 Observed Value 190 200 S. Ghosh et al. (a) Act: Q-Q Plot of IMD rain 30 (b) Act: Detrended Q-Q Plot of IMD rain 4 25 Deviation from Normal Expected Normal Value 3 20 15 10 2 1 0 5 0 -1 0 10 20 30 40 0 10 20 Observed Value (c) Act: Q-Q Plot of Mix99 rain (ERSST) 15 2 Deviation from Normal Expected Normal Value 40 (d) Act: Detrended Q-Q Plot of Mix99 rain (ERSST) 3 10 5 0 1 0 -5 -1 -5 0 5 10 15 0 2 4 Observed Value 15 30 Observed Value 6 8 10 12 14 Observed Value (e) Act: Q-Q Plot of Mix99 rain (OI_WK) (f) Act: Detrended Q-Q Plot of Mix99 rain (OI_WK) 2 Deviation from Normal Expected Normal Value 10 5 1 0 0 -1 -5 0 5 Observed Value 10 15 0 5 10 15 20 Observed Value Fig. 11 a–l Q-Q distribution of the individual datasets with 1% significant band along with the detrended Q-Q distribution of the respective dataset considered for active phase simulation Aspect of ECMWF downscaled Regional Climate Modeling in simulating Indian summer monsoon rainfall and... (h) Act: Detrended Q-Q Plot of NOAA olr (g) Act: Q-Q Plot of NOAA olr 15 260 240 Deviation from Normal Expected Normal Value 10 220 200 180 5 0 160 140 140 160 180 200 220 240 -5 140 260 160 180 290 200 220 240 260 Observed Value Observed Value (j) Act: Detrended Q-Q Plot of Mix99 olr (ERSST) (i) Act: Q-Q Plot of Mix99 olr (ERSST) 6 280 Deviation from Normal Expected Normal Value 4 270 260 250 2 0 -2 -4 240 -6 230 220 230 240 250 260 270 280 220 290 230 240 Observed Value 280 10.0 270 260 250 240 270 280 290 7.5 5.0 2.5 0.0 240 250 260 270 Observed Value Fig. 11 (continued) 260 (l) Act: Detrended Q-Q Plot of Mix99 olr (OI_WK) 12.5 Deviation from Normal Expected Normal Value (k) Act: Q-Q Plot of Mix99 olr (OI_WK) 290 230 230 250 Observed Value 280 290 300 -2.5 230 240 250 260 270 Observed Value 280 290 300 S. Ghosh et al. (a) Brk: Q-Q Plot of IMD rain 8 (b) Brk: Detrended Q-Q Plot of IMD rain 3 6 Deviation from Normal Expected Normal Value 2 4 2 0 1 0 -2 -4 -1 -4 -2 0 2 4 6 8 0 2 4 Observed Value 6 8 Observed Value (c) Brk: Q-Q Plot of Mix99 rain (ERSST) (d) Brk: Detrended Q-Q Plot of Mix99 rain (ERSST) 3 10.0 7.5 Deviation from Normal Expected Normal Value 2 5.0 2.5 1 0.0 0 -2.5 -1 -5 0 5 10 15 0 2 4 Observed Value 6 8 10 12 Observed Value (e) Brk: Q-Q Plot of Mix99 rain (OI_WK) (f) Brk: Detrended Q-Q Plot of Mix99 rain (OI_WK) 4 15 3 Deviation from Normal Expected Normal Value 10 5 2 1 0 0 -5 -1 -5 0 5 Observed Value 10 15 0 5 10 15 Observed Value Fig. 12 a–l Q-Q distribution of the individual datasets with 1% significant band along with the detrended Q-Q distribution of the respective dataset considered for break phase simulation Aspect of ECMWF downscaled Regional Climate Modeling in simulating Indian summer monsoon rainfall and... (g) Brk: Q-Q Plot of NOAA olr (h) Brk: Detrended Q-Q Plot of NOAA olr 5 300 Deviation from Normal Expected Normal Value 0 275 250 225 -5 -10 -15 -20 200 200 225 250 275 -25 180 300 200 220 240 260 280 300 Observed Value Observed Value (j) Brk: Detrended Q-Q Plot of Mix99 olr (ERSST) (i) Brk: Q-Q Plot of Mix99 olr (ERSST) 15 320 300 Deviation from Normal Expected Normal Value 10 280 260 5 0 240 220 220 240 260 280 300 -5 240 320 260 Observed Value 300 5.0 280 260 240 320 2.5 0.0 -2.5 240 260 280 Observed Value Fig. 12 (continued) 300 (l) Brk: Detrended Q-Q Plot of Mix99 olr (OI_WK) 7.5 Deviation from Normal Expected Normal Value (k) Brk: Q-Q Plot of Mix99 olr (OI_WK) 320 220 220 280 Observed Value 300 320 -5.0 230 240 250 260 270 Observed Value 280 290 300 S. Ghosh et al. Aspect of ECMWF downscaled Regional Climate Modeling in simulating Indian summer monsoon rainfall and... ƒFig. 13 a–f Empirical cumulative distribution function (ECDF) of different parameter during onset, active and break phase of ISM (Singh et al. 2014). This distribution verifies the probability by plotting quantile graphic against each other or with its theoretical distributions to understand the properties of data. The graphical representation of Q-Q plot (Fig. 9a– f) generally used to compare the CMO performance with a nonparametric approach along with their distributions with respect to in situ datasets at 5% level of significance. The scatters in x- and y-axes correspond to one of the quantile of second distribution (y-coordinate) and plotted against same quantile of first distribution (x-coordinate). If CMOs and in situ data distributions have similarity, then the quantiles will be laid approximately on the dashed line with 45o angle. Otherwise, if distributions are linearly related, then the scatters will be laid approximately on a line. But it is not necessarily to be laid on the line y = x. The red scatters indicate the distribution of ERSST CMO, while blue is for OI_WK CMO. For checking the goodness of fit of CMOs with in situ quantiles, most of the datasets during onset are near to linear line. A slight under estimation is depicted in CMO’s zonal wind speed during onset phase with respect to in situ quantiles (Fig. 9a). At the same time, an overestimation is also observed in OLR distribution among the CMOs and in situ quantiles (Fig. 9b). The wind and OLR distributions are showing a clear opposite relationship during the ISM onset. Figure 9c–f represents their goodness of fit with in situ by showing their sequentially congested distribution of data. Although, during active phase, the model-simulated output suffers from lack of rainfall distribution in comparison to the in situ and their quantiles are showing under estimation with in situ observation (Fig. 9c). On other side, due to surplus in OLR distribution with in situ, the quantiles (Fig. 9d) are showing an overestimation during active phase. During break phases, the scatters (Fig. 9e–f) are very close to in situ and the quantiles for rainfall distribution are showing goodness for CMO (Fig. 9e). The model-simulated OLR quantiles of different CMOs are found very close to the linear line (Fig. 9f). It is also observed that the ERSST CMO is showing better performance with in situ than the CMO OI_WK. On the other hand, the CMO ERSST is showing its quantiles a bit nearer to the in situ or it is overlapping over CMO OI_WK. For further verification of CMOs and in situ datasets with their respective normal quantiles, Figs. 10 and 12 have been considered for the respective parameters during the phases of monsoon. In these figures, the normal Q-Q plots represent the validation of the data with the distributional assumption of the respective dataset by providing visual comparison of the sample quantiles to the corresponding theoretical quantiles. Detrended normal Q-Q plots have also been considered for monitoring the deviation of the actual data from its normal. All the figures represent their significance with 1% significant band. The normal Q-Q plot for all the parameters of in situ as well as CMOs are plotted in Fig. 10 for onset, Fig. 11 for active phase, and Fig. 12 for break phase simulation. These figures are showing the goodness of fit with their theoretical distributions itself by illustrating their fitness over the linear line with 1% significant band (Fig. 10a, c, e, g, i, and k). The detrended normal Q-Q plots show a goodness of fit for every monsoon phase by representing the crowds of scatters from the normal. It has been also observed that the deviation of CMO data from its normal is much less than in situ (Fig. 10b: IMD rain, Fig. 10h: NOAA OLR) distribution either in the case of rainfall (Fig. 10d, f) or OLR (Fig. 10j, l). With a slight deviation from the normal, the quantiles are gathered together over the line during active and break phases and their detrended normal are also showing a high significance with less deviation (Fig. 11a–l and Fig. 12a–l). ECDF (Fig. 13a–f) is another nonparametric statistical estimator tools to interpret similar groups of random datasets by their own distributions (Gibbons and Chakraborti 2003, 1992). In this ECDF estimator, it is possible to visualize the data series distribution by the slope of the line and with the increase of ECDF from 0 to 1. Because of the limited number of data series, the curve in Fig. 13a, b looks rough rather than smooth. The ECDF fitted results of three distributions (in situ, ERSST, and OI_WK) for every single parameter is presented in one box. These boxes of different parameters and different phases showed that the CMO ERSST and CMO OI_WK are very close to the in situ distribution and following the pattern of in situ for every ECDF key quantile. The overall statistical study shows a clear distribution pattern for all the synoptic parameters in CMO ERSST and OI_WK considered for the analysis of onset, active, and break phase with respect to the in situ. Their distribution patterns are showing goodness of fit with in situ observation and their theoretical distributions itself. This section deals with the validation of the RegCM output with in situ during different phases with statistical inference which performs well in comparison to in situ. In other words, the RegCM follows the ICBC with some over/under estimation in the model simulation. 4 Summary and conclusions The study indicates the sensitivity of the RegCM simulation for ISM with different lateral boundary conditions. In order to evaluate the causative factors behind the uncertainties in the S. Ghosh et al. RegCM simulation, various factors are considered and subsequently analyzed. The conclusions of this study are as follows: Sensitivity of RegCM’s Mix99 (Grell->Land & Emanuel->Ocean) CPS has a minor dependency with the ERSST and OI_WK SST lateral boundary conditions in simulating the intraseasonal variability of ISM. The zonal wind of EIN15 reanalysis ICBC lacks enough speed over the core Indian region and around Kerala during definite rainy days (onset and active phases) of ISMR. Model-simulated rainfall distribution entirely depends on the distribution of in situ ICBC and the disturbed zonal wind distribution in EIN15, which is being carried forward in the RegCM simulation process. Statistical distribution of all the parameters are in agreement with their respective theoretical quantiles and in situ distribution, and CMOs are following the pattern of in situ datasets with some under/over estimation, which illustrates the RegCM capabilities towards ISM simulation. The RegCM is able to capture the prominent data distribution very well during the phases of monsoon, if an unbiased ICBC is provided. Therefore, it is advised to cross verify the ICBC over the specified study region before using it directly for the RegCM simulations. Acknowledgements This work is a part of a R&D project, funded by the Department of Science and Technology (DST), Ministry of Earth Science (MoES), Govt. of India. The authors wish to thank to The India Meteorology Department (IMD), NOAA/OAR/ESRL (Boulder, Colorado, USA; http://www.esrl.noaa.gov/psd/), and European Centre for Medium-Range Weather Forecasts (ECMWF) for providing gridded datasets. The authors seem their sincere gratitude to Prof. T.N. Krishnamurti, Florida State University, USA for his valuable comments on the manuscript to improve publication quality. Special thanks to the International Center for Theoretical Physics (ICTP), Italy, for providing the RegCM. The authors wish to extend their sincere gratitude to the Journal Editor and the Reviewers for their insightful comments on the paper. References Almazroui M (2016) RegCM4 in climate simulation over CORDEXMENA/Arab domain: selection of suitable domain, convection and land-surface schemes. Int J Climatol 36:236–251 Almazroui M (2012) Dynamical downscaling of rainfall and temperature over the Arabian Peninsula using RegCM4. 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