Impact of revised cloud microphysical scheme in CFSv2 on the simulation of the Indian summer monsoon

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. (2015) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: /joc.4320 Impact of revised cloud microphysical scheme in CFSv2 on the simulation of the Indian summer monsoon Anupam Hazra,* H. S. Chaudhari, S. A. Rao, B. N. Goswami, A. Dhakate, S. Pokhrel and S. K. Saha Indian Institute of Tropical Meteorology, Pune, India ABSTRACT: Role of the cloud parameterization scheme and critical relative humidity (RHcrit) for large-scale precipitation is examined for simulating Indian summer monsoon (ISM) by the National Centers for Environmental Prediction (NCEP) climate forecast system version 2 (CFSv2). The major biases of the model simulations namely dry bias over the major continents, cold tropospheric temperature (TT) bias and cold sea surface temperature (SST) bias are related to biases in distribution of clouds. This study evaluates the role of variable RHcrit to get better simulation of high level clouds and reduce TT bias and cloud microphysical parameterization to improve the meridional gradient of TT towards achieving better simulation of south Asian monsoon precipitation. Sensitivity experiments of CFSv2 with the modified RHcrit and cloud microphysical scheme compared to the control simulation show that while the RHcrit leads to some development of the cloud distribution and contributes to some progress of the dry bias over India, the cloud microphysics changes lead to a significant improvement of the cloud simulations. Particularly, revised cloud microphysics scheme coupled with modified RHcrit results in a much improved global distribution of cloud fraction with zonal mean cloud fraction being close to observation. This leads to significant improvement in the meridional gradient of TT leading to rainfall over south Asian monsoon region. The dry bias is not only reduced over the Indian subcontinent but also over other regions of global tropics such as the central Africa and the northern South America. The annual cycle of all India rainfall is in good agreement with observation not only in amount but also in the onset and withdrawal phases. Thus, modifications in the cloud microphysical parameterization scheme in CFSv2 have played a vital role in simulation of the ISM in particular. The sensitivity experiments demonstrate the betterment of the mean monsoon and may lead to help improve monsoon forecasts. KEY WORDS CFSv2; cloud microphysical scheme; critical relative humidity; tropospheric temperature gradient; Indian summer monsoon Received 10 October 2014; Revised 2 February 2015; Accepted 25 February Introduction Monsoon rainfall is the major prerequisite of agricultural productivity in many tropical and subtropical regions of the world, and its variability has been affecting the livelihoods of a majority of the world s population (e.g. Parthasarathy et al., 1988; Gadgil and Gadgil, 2006). The coupling between the ocean and atmosphere is crucial on seasonal to interannual time scales and hence a coupled atmosphere ocean general circulation model (AOGCM) is required for prediction on these time scales. During the past couple of decades, development of dynamical coupled ocean atmosphere model prediction systems have led to important advances in the tropical seasonal prediction (Kumar et al., 2005; Wang et al., 2005; Kug et al., 2008; Chaudhari et al., 2013a; Saha et al., 2014a). Despite the advances made in the development of coupled climate models, dynamical forecast of the Indian summer monsoon rainfall (ISMR) remains a challenging problem * Correspondence to: A. Hazra, Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pashan, Pune , India. hazra@tropmet.res.in (e.g. Sperber and Palmer, 1996; Webster et al., 1998; Sperber et al., 2001; Wang et al., 2005; Pokhrel et al., 2012a; Chaudhari et al., 2013a). While the climate models can now predict boreal summer precipitation over most of the tropics one season in advance with high skill, the correlation between prediction and observation of summer precipitation over the Asian monsoon region remains close to zero (Kumar et al., 2005; Wang et al., 2005). With further improvements, the newer generation of models (e.g. the models involved in ENSEMBLES project compared to those involved in DEMETER project) are achieving the correlation between observation and prediction of seasonal mean precipitation over India to better than 0.3 (Preethi et al., 2010; Rajeevan et al., 2012). However, the skill of prediction of the south Asian monsoon rainfall by all models currently remains significantly below the potential limit on predictability (Goswami, 1998; Goswami et al., 2006). Several reasons are likely to contribute to this problem. (1) El Nino-Southern Oscillation (ENSO) being a major driver for the Asian monsoon, systematic errors by models in simulating the ENSO-monsoon teleconnection (Annamalai et al., 2007) contributes to poor skill of monsoon prediction. (2) The models must also simulate the 2015 Royal Meteorological Society

2 A. HAZRA et al. observed climate noise or internal interannual variability correctly. Models either overestimate or underestimate this, leading to either undermining the ENSO influence or overplaying it. (3) Finally, almost all models have serious systematic biases in simulating the observed climate over the Asian monsoon region (Pokhrel et al., 2012b; Pokhrel et al., 2013; Sabeerali et al., 2014). This systematic bias is likely to be to a major contributor to the poor skill of monsoon forecasts. In this study, we attempt to improve some of the systematic biases of one prediction model, climate forecast system version 2 (CFSv2, Saha et al., 2010, 2014b) developed by the National Centers for Environmental Prediction (NCEP). The model has been selected as a base model for the future development of a reliable prediction system of ISMR under the ambitious Monsoon Mission Project by the Ministry of Earth Sciences, Government of India. The selection of this model for operational seasonal forecasting in India has been based on the fact that the hindcast skill of this model for prediction of ISMR is one of the best (Figure 1(a)). The latest version of the model has shown considerable improvement in various aspects of ISMR dynamics (Saha et al., 2014a). However, CFSv2 simulations are still associated with several biases over the region (Saha et al., 2014a). The model (CFSv2) control (CTL) simulation exhibits dry rainfall bias over Indian landmass in the CTL run (Saha et al., 2014a) which can be a setback for realistic representation of Indian summer monsoon (ISM) rainfall. The model simulations have a significant bias in the tropospheric temperature (TT, averaged between 600 and 200 hpa, Figure 1(b)). As previous studies (e.g. Webster et al., 1998; Goswami and Xavier, 2005; Xavier et al., 2007) have shown that the north south gradient of TT (ΔTT) is very important in order to sustain the monsoon circulation, it is closely linked with the seasonal precipitation amount. The ΔTT is also closely linked with the onset and withdrawal of the ISM (Ueda and Yasunari, 1998; Goswami and Xavier, 2005; Xavier et al., 2007). Thus, TT is an important parameter to check the model s ability for the realistic representation of the monsoon onset, sustenance and withdrawal. It is interesting to note that the cold TT bias is larger to the north of the equator as compared to the equatorial belt (Saha et al., 2014a) where it is relatively less. This indicates that the spatial distribution of TT bias would lead to a weakening of the north south gradient of the TT over the region and subsequently leading to weakening of the Indian monsoon (e.g. Goswami and Xavier, 2005). The ISM being a coupled ocean atmosphere system and significant biases in simulating the sea surface temperature (SST) and Eurasian snow are likely to have adverse influence on the air sea interaction processes, and hence it can also affect ISMR simulation (e.g. Rao et al., 2010; Chaudhari et al., 2013b; Saha et al., 2013). Figure 1(c) suggests that CFSv2 CTL run exhibits basin wide cold SST bias (Pokhrel et al., 2012a; Saha et al., 2014a). Some of the other important biases of the model are highlighted in Figures 1(d) (e) and 2(a) (c). Another bias which is generic to many models and which produces too much small scale convective precipitation (Dai, 2006; Saha et al., 2014a) and too little large-scale stratiform precipitation (Saha et al., 2014a). The ratio between convective to total rain in model simulations (Saha et al., 2014a) shows that the contribution from stratiform clouds is between 10 and 20% while that in observation is more than 40 60% (Figure 1(d) and (e)). A related problem of the model simulations is that the production of cloud hydrometeors (cloud water and ice) in CFSv2 with original microphysical scheme (CTL) is inconsistent with observations (Figure 2(a) (c)). The vertical structure of cloud along with cloud hydrometeors (e.g. cloud water and cloud ice) associated with ISM and its intra-seasonal variability are important (Halder et al., 2012; Rajeevan et al., 2013). The interaction among thermodynamics, cloud microphysics and dynamics plays a crucial role on the summer monsoon precipitating clouds (Hazra et al., 2013a, 2013b; Kumar et al., 2014). Waliser et al. (2009) have pointed out that the inadequacies in representations of these clouds have impacts on latent and radiative heating and results in inadequacy in circulation and hydrological cycle. The understanding of the representation of some microphysical processes like tendency (production rate) of auto-conversion and accretion from cloud water to rainwater in the warm phase, freezing and accretion of snow in the mixed phase cloud is essential (Sundqvist et al., 1989; Zhao and Carr, 1997; Waliser et al., 2009; Hazra et al., 2013a, 2013b; Kumar et al., 2014). The physics involved in our modification is presented as a simplified block diagram (Figure 2(d)). In particular, upper level cloud hydrometeors are underestimated (Figure 2(c)) as compared to CloudSat (Figure 2(a)) and MERRA reanalysis (Figure 2(b)). These biases in the production of cloud hydrometeors also have a large impact on the vertical profile of latent heating (Tao et al., 1990; Hazra et al., 2013a, 2013b; Abhik et al., 2014; Kumar et al., 2014) in terms of weakening the updraft and convection. As a feedback, cloud water cannot be uplifted to above the freezing level. Therefore, there is more cloud water at lower level which further reduces the size of cloud drops (smaller cloud drops), thereby weakening the collection efficiency for rain formation (Tao et al., 1990; Hazra et al., 2013a, 2013b; Kumaret al., 2014). Again cloud drop size is important for the evaporation rate of cloud water (Mason, 1971; Pruppacher and Klett, 1997) because smaller cloud drops evaporates faster (Tao et al., 2012; Kumar et al., 2014). The cloud water evaporation again impacts on the calculation of latent heating (Tao et al., 1990). The vertical structure of latent heating is crucial for the precipitating clouds as it can influence the large-scale dynamics in a very significant way (Chattopadhyay et al., 2009; Choudhury and Krishnan, 2011; Abhik et al., 2014; Kumar et al., 2014). We envisage that above mentioned biases are linked to each other, and proper targeted improvement of one may lead to improvement in one or more of other biases as well. For example, proper simulation of cloud hydrometeors may lead to better cloud distribution, which in turn may lead to correct representation of large-scale circulations

3 REVISED CLOUD MICROPHYSICAL SCHEME IN CFSV2 FOR SIMULATION OF ISMR Figure 1. (a) Taylor plot showing the skill of models in simulating mean seasonal cycle over Indian land points. (b) Tropospheric temperature (TT in K) and (c) sea surface temperature (SST in C). The convective to total rainfall ratios is also shown for (d) observation (TRMM) and (e) CFSv2 (control, CTL). and consequently it may modulate the TT that plays a major role in sustaining the strength of the Indian monsoon (Xavier et al., 2007). With this background, we attempt to test the change in tendency equations of cloud parameterization scheme in the model. Two different ways for inspecting CFSv2 simulations are considered, namely (1) critical relative humidity (RHcrit) in the clouds formulation of the model (i.e. 85%) is responsible for a part of the TT bias and probably a part of the dry bias over Indian land mass, (2) a revised representation of microphysical tendency equations for convective clouds suggested by Sundqvist et al. (1989) for better depiction of the TT gradient (ΔTT) and precipitation calculation. The basis for the first experiment is that a lower RHcrit reduces moisture residency time in the atmosphere, weakens green house effect, enhances long wave radiation loss to space and thereby leads to a cold TT bias. Recently, several studies have shown the benefit of a proper prescription of RHcrit for the betterment of coupled ocean atmosphere simulations (Sanderson et al., 2010). Three sensitivity experiments are performed by the adjustment of RHcrit for potential improvement of climatological representation of clouds in this study. The second attempt is driven by the need for proper production of cloud hydrometeors in the upper atmosphere in the tropics. Aiming to improve the ice/water mixing ratio simulations by the model, we test sensitivity experiments with cloud microphysical parameterization in the model. The change in microphysical tendency equations (e.g. auto-conversion, accretion, freezing rate) is expected to get better conversion of cloud water to rain water, ice and mixed-phase for precipitation formation. Presently, CFSv2 considers a simple cloud microphysics parameterization of Zhao and Carr (1997), which in turn is based on Sundqvist et al. (1989). Sundqvist et al.

4 A. HAZRA et al. (a) CloudSat-IWC (mg m 3 ) (b) MERRA-QIC (mg kg 1 ) (c) CFSv2 (CTL) CLWMRprs (mg kg 1 ) (d) Microphysical tendencies or production rates (Autoconversion, accretion, freezing, deposition, condensation, melting, evaporation) Precipitation Production of rain (P) = autoconversion + accretion + melting Dynamics (convection) Temp. Vertical profile of latent heating/cooling Latent heating/cooling = condensation + accretion + freezing + deposition + ( melting evaporation) Figure 2. Vertical profile of longitudinal average (longitude: E) of JJAS mean of cloud ice/water mixing ratio for (a) CloudSat ice water content (IWC in mg m 3 ), (b) MERRA cloud ice mixing ratio (QIC in mg kg 1 ) and (c) model simulated cloud ice/water mixing ratio (CLWMRprs in mg kg 1 ) with original cloud scheme (CTL). (d) The physical processes involved in the present experiment and interaction/feedback between cloud microphysics to dynamics is presented as a simple block diagram. (1989) proposed two parameterization formulations for auto-conversion rates (1) for stratiform cloud, presently used in CFSv2 (abbreviated as Control or CTL), and (2) for convective cloud, revised in CFSv2 (named as revised or revzc). The tendency equations of cloud water to rain water auto-conversion (P raut ) based on Sundqvist et al. (1989) and Zhao and Carr (1997) are given below: { P raut = c 0 q c 1 exp ( q c b ) } 2 (1) where, constants c 0 and b are taken from Sundqvist et al. (1989) and q c is the cloud mixing ratio. Another important process that converts cloud water/ice to precipitation is the collection of cloud substance by falling precipitation. At the same time, the accretion/riming rate is also likely to help for better generation of rain although the collision with mixed-phase processes (Zhao and Carr, 1997; abbreviated as ZC ) which was turned off in the CTL is now turned on in the revised microphysical scheme (revzc). Thus, the accretion term P racw, which is the generation rate of rain due to collection of cloud water (rain accretion) is also given below: P racw = E cr q c P rprc (2) where, E cr is the collection efficiency parameterized based on Zhao and Carr (1997). q c is the cloud mixing ratio and P rprc is the rainfall rate. All these changes in production rates are expected to eventually modify latent heating profile (Tao et al., 1990) and thereby provide feedback to dynamics (Abhik et al., 2014; Kumar et al., 2014). In order to see the combined influence of the two modifications, we have conducted another set of sensitivity experiments with revised microphysics scheme (revzc) together with the variable RHcrit. It may help in depiction of representation of clouds (cloud cover and cloud condensate) which may have impact on TT and ΔTT, and it cloud get manifested in ISM precipitation. For a long time, the cloud microphysics on cloud properties and latent heating has been considered to play a dominant role on the climate simulation reported by various researchers (e.g. Li and Le Treut, 1992; Gregory and Morris, 1996; Penner et al., 2001; Gettelman et al., 2010). This is the first time in CFSv2 we have shown evidence that the role of cloud microphysics and its feedback to dynamics play a significant role in simulating the observed global climate in general and south Asian monsoon in particular. This paper is organized in the following manner. The model description, design of numerical experiments and datasets used are elaborated in Section 2. Results and discussions are presented in Section 3. The findings of the study are concluded in Section Model 2.1. Model description NCEP has developed the CFSv2 (Saha et al., 2010, 2014b) fully coupled ocean atmosphere land model with advanced physics, increased resolution and refined initialization to improve the seasonal climate forecasts. NCEP CFSv2 consists of a spectral atmospheric model at a resolution of T126 ( ) with 64 hybrid vertical levels and the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model, version 4p0d (Griffies et al.,

5 REVISED CLOUD MICROPHYSICAL SCHEME IN CFSV2 FOR SIMULATION OF ISMR 2004) at grid spacing with 40 vertical layers. The atmosphere and ocean models are coupled with no flux adjustment. It utilizes simplified Arakawa Schubert (SAS) cumulus convection (Pan and Wu, 1995; Hong and Pan, 1998) with momentum mixing. It implements orographic gravity wave drag based on Kim and Arakawa (1995) approach and sub-grid scale mountain blocking by Lott and Miller (1997). It uses rapid radiative transfer model (RRTM) shortwave radiation with maximum random cloud overlap (Iacono et al., 2000; Clough et al., 2005). It is also coupled to a four-layer Noah land surface model (Ek et al., 2003) and a two-layer sea ice model (Wu et al., 2005). In addition, cloud condensate is a prognostic variable (Moorthi et al., 2001) with a simple cloud microphysics parameterization (Sundqvist et al., 1989; Zhao and Carr, 1997). Both large-scale condensation and the detrainment of cloud water from cumulus convection provide sources of cloud condensate (Moorthi et al., 2010; Sun et al., 2010). The convective source term is provided by the cloud top detrainment in the convective parameterization (Moorthi et al., 2001; Moorthi et al., 2010). The large-scale condensation is based on Zhao and Carr (1997), which in turn is based on Sundqvist et al. (1989). The separation of cloud water and ice is based on temperature alone. The fractional cloud cover used in the radiation calculation is diagnostically determined from the predicted cloud condensate based on the approach of Xu and Randall (1996). The diagnostic of cloud fraction from CFSv2 is approximations of the model 3-D clouds (Moorthi et al., 2001, 2010; Sun et al., 2010). The boundaries of the domains are at 65 and 400 mb for lower latitudes, and it decreases linearly from mid- to high latitudes (750 and 500 mb). Clouds fractions are computed within each domain by max-random overlapping assumption, and time averaged values are considered (Moorthi et al., 2010; Sun et al., 2010; Saha et al., 2014b). The details of the model description is available in previous studies (Pokhrel et al., 2012a; Saha et al., 2014a, 2014b) and presently used for ISMR forecast (e.g. Abhilash et al., 2014) Design of experiment Experiment of ISM precipitation simulation is attempted by implementing two different approaches (1) Better climatological representation of clouds by physically based adjustment of RHcrit and (2) revised the tendency equations of auto-conversion rates for convective clouds as suggested by Sundqvist et al. (1989) and turned-on rain accretion rate (Zhao and Carr, 1997) in the cloud microphysical scheme (abbreviated as revzc ). As suggested by Sundqvist et al. (1989), relative humidity-based cloud schemes assume clouds formation after the relative humidity reaches a critical value (usually around RHcrit of 80%). In general, RHcrit is empirically adjusted within the cloud cover formulation to mimic the observation or to adjust the long-term climatological simulations (Walcek, 1994). This tuning of RHcrit within a physically plausible range gives a better representation of cloud processes and also produces a better resemblance with observation (Slingo et al., 1987; Sanderson et al., 2010). Recently, Sanderson et al. (2010) has implemented variable RHcrit (65, 73, 90%) during their Coupled General Circulation Model (CGCM) studies. Thus, uncertainty involved in representation of clouds has motivated researchers to evaluate and improve methods for better elucidation of cloud processes. Because CFS uses a constant value of RHcrit (85%), it may not be suitable for tropical region and in particular for the monsoon region. This was the motivation behind our sensitivity experiments with different RHcrit (88, 90, 89%) in CFSv2 model, where it was previously 85%. More details of choosing the RHcrit tunable parameter for ISM simulation is described in De et al. (2014). In their work, they have used radio-sounding observation for many stations (e.g. Pune, Mumbai, Kolkata, Ranchi, Bhubaneswar, New Delhi, Patna, Vishakhapatnam, Lucknow) along with reanalysis data (De et al., 2014). In this present study, three sets of sensitivity experiments are performed by specifying the different values of RHcrit (tunable parameter). Control (CTL) run: value of RHcrit is set as 85, 85, 85% for low, mid, highlevels, respectively (default values specified in CFSv2). CRH90 run: value of RHcrit is set as 90, 90, 90%. CRHvari run: value of RHcrit is set as 88, 90, 89%. The first experiment with default RHcrit (85%) termed as CTL run. The second experiment is carried out (e.g. CRH90) in an attempt to tune the cloud simulations by the model to climatological values. However, in reality, value of RHcrit is variable for low, mid and high levels, and therefore it might be useful for the model to have implementation of a vertically variable RHcrit (e.g. Sanderson et al., 2010). To incorporate it, third experiment is performed which is called as variable RHcrit (CRHvari). Second, another set of sensitivity experiment with revised microphysics scheme (revzc, Sundqvist et al., 1989; Zhao and Carr, 1997) together with better RHcrit namely the CRHvari (revzc + CRHvari) is performed. In each experiment, the CFSv2 has been initialized by the same initial condition, and model is integrated for 10 years. June September (JJAS) climatological mean of required fields are presented in these analysis. The CFSv2 has been ported on IBM Prithvi High Performance Computing system at Indian Institute of Tropical Meteorology (IITM), Pune. Initial conditions for the atmosphere and the ocean are derived from NCEP Climate Forecast System Reanalysis (CFSR; Saha et al., 2010; Chaudhari et al., 2014) Observed datasets used Rainfall datasets from the Global Precipitation Climatology Project (GPCP; Adler et al., 2003) is used in this study. Monthly winds, specific humidity and mean sea level pressure (MSLP) from NCEP reanalysis version-2 (Kanamitsu et al., 2002) are utilized in the analysis. Reynolds version-2 SST monthly data (Reynolds et al.,

6 A. HAZRA et al. Figure 3. Distribution of JJAS mean of high cloud fraction over global tropics from (a) CALIPSO observation, (b) CFSv2-CTL (control; CRH85), (c) CFSv2-CRH90 and (d) CFSv2-CRHvari. 2002) are utilized in the present study. The cloud fraction and cloud phase information from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) instrument on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite are used here (Hu et al., 2009; Winker et al., 2009; Thorsen et al., 2011). The algorithm for CALIPSO product is available in Winker et al. (2009). The monthly JJAS climatology product of cloud fraction from 2006 to 2012 has been used to calculate high cloud fractions. The Modern Era Retrospective Analysis for Research and Application Analysis (MERRA) monthly products between 2001 and 2011 have been used to calculate mean vertical structure of cloud ice and cloud water, and detailed description of that data set is available in Rienecker et al. (2011). Ice water content from CloudSat daily products (Waliser et al., 2009) for JJAS period has been used in this study. Presently, we have obtained the data from CloudSat group only for the regions of longitude: E and latitude: 30 S 40 N. 3. Results and discussions 3.1. Role of RHcrit and modified cloud scheme in modulating high cloud cover and cloud mixing ratio Previous studies (e.g. Smith, 1990) have pointed out that tuning of RHcrit has a direct effect on cloud formation as relative humidity plays a cardinal role in the formation of clouds (Slingo and Ritter, 1985). The three categories of clouds namely high, middle and low are also modified considerably as per the sensitivity experiment. CFS represents these fractional cloud cover diagnostically determined by the predicted cloud condensate based on the approach of Xu and Randall (1996) with no overlapping between them. The three layers are defined vertically between pressure levels (Hu et al., 2008). JJAS mean of high cloud fraction simulated in the CTL and RHcrit experiments is shown in Figure 3 along with observations from CALIPSO (Figure 3(a)). Our focus is mainly on global tropics and therefore we have represented only 40 S 40 N. It is clear that the CFSv2 in the CTL underestimates the high level clouds (Figure 3(b)). The modified RHcrit experiments (Figure 3(c) and (d)) enhance the high level clouds by a small percentage ( 3%) and make it closer to the observation over inter tropical convergence zone (ITCZ). JJAS mean of high cloud fraction from model simulation with revised microphysical scheme (revzc) is shown in Figure 4(a). High cloud cover, which was underestimated in original cloud scheme (CTL), is now improved significantly in modified cloud scheme (revzc, Figure 4(a); and revzc + CRHvari, Figure 4(b)) as compared to observation (Figure 3(a)). Thus, betterment of high level cloud fraction in CFSv2 through implementation of the modified cloud scheme (revzc and revzc + CRHvari) indicates for the better simulation of TT. The other interesting point is that this development of high cloud fraction bias is not limited over only Indian subcontinent alone. The underestimation of high cloud fraction in other regions of global

7 REVISED CLOUD MICROPHYSICAL SCHEME IN CFSV2 FOR SIMULATION OF ISMR Figure 4. Distribution of JJAS mean of high cloud fraction over global tropics from CFSv2 model simulation for (a) revzc (revised microphysics scheme) and (b) revzc + CRHvari (revised microphysics scheme + CRHvari) sensitivity experiments. tropics (especially over continental regions of central Africa and northern South America) is now also improved significantly (Figures 3 and 4; marked by dotted circle). The zonal and JJAS mean of three categories of clouds viz. high, middle and low from the model with modified microphysical scheme are compared with CALIPSO observation in (Figure 5(a) (c)). High cloud cover, which was underestimated in original cloud scheme (CTL), is now improved significantly in revzc + CRHvari experiment as compared to observation (Figure 5(a)). The high cloud cover bias averaged over ISM region (longitude: E; latitude: 10 S 35 N) is reduced from 15.5% (for CTL) to 7.9% (for revzc + CRHvari). The biases in the mid-level cloud fraction is also changed by the revised/modified cloud microphysical scheme (revzc + CRHvari) where the serious overestimation in the CTL run is now slightly underestimated, but it is close to the observation (Figure 5(b)). However, the revised microphysics leads to an underestimation of the low level clouds as compared to observation (Figure 5(c)). Hu et al. (2008) have pointed out that the low level cloud fraction in the CFS is not exactly the same as in the observation [e.g. International Satellite Cloud Climatology Project (ISCCP)]. The former refers to the clouds above the Boundary Layer (BL) but below 650 hpa, while the later below the pressure level of 680 hpa (Hu et al., 2008). Their results suggest that CFS low cloud amount is persistently low compared with that in the observation, even with the inflated definition in the CFS. Therefore, further experiments are required with convective and radiation parameterization schemes to improve this aspect of low level clouds simulation. The vertical profile of differences between RHcrit sensitivity experiments and the CTL run for the total cloud condensate averaged over the south Asian monsoon region (10 30 N, E) shows that the CRHvari enhances cloud water mass at upper levels by lowering the same at lower levels compared to CTL run (Figure 6(a)). This is a little improvement leading to the enhanced high cloud fraction in case of CRHvari experiment (Figure 3(d)). The latitudinal variation of vertical profile of the cloud condensate during JJAS for revzc + CRHvari experiment is shown in Figure 6(b), indicating a substantial development in the cloud condensate pattern, although some underestimation in the north of equator in the upper atmosphere remains. The modified cloud scheme modifies the bifurcation of total cloud condensate into cloud water and cloud ice. As the convective clouds develop, it helps to lift more moisture to upper levels (above freezing level) which in turn produces more cloud ice (Figure 6(b) and (c)). Conversely, as water vapour is uplifted, the amount of cloud water is decreased at lower levels. High cloud feedback also reflects production of more cloud ice due to modified cloud microphysical scheme (Figure 4(a) and (b)). As a result, the modified microphysics scheme leads to a reduction in the cloud water at low and middle level and an increase in cloud ice at the upper level (Figure 6(c)). It is also noticed that there is no condensate up to around 800 hpa in the revzc + CRHvari experiment (Figure 6(c)). There may be two possible reasons behind this namely, (1) as the auto-conversion rate of cloud water to rain water is modified, some part of the cloud water may convert into rain water and thus precipitate out and (2) part of the cloud water may be lifted below the freezing level and thus converted to cloud ice. Further research work is required to reduce this shortcoming particularly for the formation of cloud condensate at lower level with other microphysical processes such as freezing and evaporation (Trenberth et al., 2003; Hazra et al., 2013a, 2013b; Padmakumari et al., 2013; Hazra et al., 2014). Better cloud distribution is likely to have substantial impact on the TT and consequently may reflect on improved large-scale circulation. In this respect, following sections ( ) present results of RHcrit and revzc scheme experiments by presenting details of TT, SST, upper and lower tropospheric circulation features and rainfall distribution.

8 A. HAZRA et al. Figure 5. The global JJAS mean of different cloud cover for (a) high, (b) mid and (c) low level cloud for control (CTL), revzc + CRHvari (revised microphysics scheme + CRHvari) sensitivity experiments compared with CALIPSO observation TT and its north south gradient As discussed above, introduction of new RHcrit may modulate the TT through cloud feedback and subsequently may lead to proper strength of TT gradient for establishment of realistic monsoon. In this respect, the model simulated global TT bias (CTL experiment minus observation) is shown in Figure 7(a). In addition to the cold bias noted in CTL experiment over the region of Indian subcontinent (Saha et al., 2014a), there is pervasive cold bias throughout the globe with large cold bias in the high latitudes. This cold bias is reduced in the CRHvari experiment (Figure 7(b)) which is little bit closer to the real world. Although the severity of cold TT bias is reduced to some extent due to RHcrit tuning (Figure 7(c)), there still exits considerable cold bias which may be due to feedback from other processes yet to be deciphered. Therefore, modified microphysics is the urgent need for the further improvement of cold biases. The JJAS mean TT bias (model minus observation) for the modified microphysics with CRHvari (revzc + CRHvari) is presented in Figure 7(d). The strong cold bias in CTL run (Figure 7(a)) exhibited over both the poles, particularly over the North Pole is now reduced significantly in modified microphysics run (Figure 7(d)). In the global tropical region, there is a warm TT bias because of production of higher cloud condensate at upper level in the modified cloud microphysical scheme (already discussed in sub-section 3.1). The increase of TT in the revzc + CRHvari simulations also indicates that there must be overall enhancement of the simulation of the vertical profile of temperature globally. This is indeed the case as seen from Figure 8(a) and (b), where vertical profile of observed temperature (T) compared with that simulated by the CTL run and the revzc + CRHvari run are shown in two different locations (B1 box: E and N; B2 box E, N). Temperature is cooler than observed throughout the troposphere in the CTL run while it is very close to observation with a slight warm bias in the revzc + CRHvari run. From the feedback of microphysical process, cloud water uplift below freezing level and enhance condensation and deposition processes, all of which together are responsible for the heating part (warm bias). For the south Asian monsoon, the TT gradient (ΔTT) in the region is strongly associated with the strength and onset and withdrawal of the monsoon (Goswami and Xavier, 2005; Xavier et al., 2007). While the onset ( withdrawal ) of south Asian monsoon is related to the transition of ΔTT from negative to positive (positive to negative), the strength of positive ΔTT is related to the strength of the seasonal mean monsoon (Xavier et al., 2007). The annual cycle of the north south TT difference (ΔTT) as defined by Goswami and Xavier (2005) from observation and the three simulations (CTL, RHcrit and revzc + CRHvari) is shown in Figure 8(c). It may be noted that in the CTL simulations, the onset is delayed and withdrawal is early and the positive part of ΔTT is weaker than observed, consistent with the dry bias and weaker monsoon (Figure 8(a)). It is also noted that improvement of ΔTT is not significant (it is not a statistically significant change) when we consider the case of RHcrit only (Figure 8(c)). Interestingly, however, modified microphysics shows significant improvement in ΔTT (Figure 8(c)), which is very close to the observation particularly during monsoon season (JJAS). It is also noticed that the transitions of ΔTT are also very close to observation which are related to good simulation of onset and withdrawal dates of ISM in the revzc + CRHvari experiment. Thus, only RHcrit is not enough for the improvement of the north south gradient of TT, and modified cloud microphysical scheme is instrumental in getting realistic values of ΔTT. Improvement of ΔTT is also expected to improve precipitation formulation Sea surface temperature (SST) The JJAS mean SST bias for global tropics and sub-tropics is presented for the original CTL run (CTL), CRHvari,

9 REVISED CLOUD MICROPHYSICAL SCHEME IN CFSV2 FOR SIMULATION OF ISMR Figure 6. (a) Vertical profile of the differences (sensitivity minus control) of total cloud condensate (cloud water and cloud ice, unit: mg kg 1 ) is shown for CRH90 minus CRH85 (black line), and CRHvari minus CRH85 (red line with symbol). (b) Zonal annual mean of cloud ice/water mixing ratio (mg kg 1 ) at different pressure level for CFSv2 with modified cloud microphysical scheme with CRHvari (revzc + CRHvari) and (c) vertical profile of seasonal (JJAS) mean and all India averaged (70 90 E and N) cloud ice/water mixing ratio (mg kg 1 ) from MERRA, control (CTL) and revzc + CRHvari. Figure 7. Global spatial pattern of JJAS mean tropospheric temperature-bias (model minus observation) for sensitivity experiments (a) control, (b) CRHvari, (c) difference between CRHvari and CTL and (d) modified cloud microphysical scheme with CRHvari (revzc + CRHvari) Royal Meteorological Society Int. J. Climatol. (2015)

10 A. HAZRA et al. Figure 8. Vertical profile of temperature from model with sensitivity experiment compared with observation (NCEP) (a) averaged over (longitude: E and latitude: N, B1 box) and (b) averaged over (longitude: E and latitude: N, B2 box). The boxes are indicated in Figure 5(a). Tropospheric temperature difference (ΔTT) between two boxes [zonal extent of both boxes is between E while meridional extent of the northern (southern) box is N(15 S 10 N)] is also shown here. (c) Annual cycle of ΔTT for Modified cloud microphysical scheme with CRHvari (revzc + CRHvari, red line), CRH90 (green dash line), CRHvari (green dotted line) compared with control (CTL, blue line) and observations (OBS, black line). with modified microphysics (revzc) and with CRHvari plus modified microphysics (revzc + CRHvari) in Figure 9(a) (d). As mentioned earlier, the CTL run has pervasive cold bias with strong biases over the sub-tropical gyre regions with warm biases in the eastern equatorial Pacific and Atlantic (Figure 9(a)). The CRHvari improves the cold bias marginally over most places but fails to improve it significantly (Figure 9(b)). However, the experiment with modified microphysics shows considerable enhancement in SST leading to warm bias in many places (Figure 9(c)). The warm bias is small and within tolerable limit in most of deep tropics. It is significant in the north Pacific extra tropics. It is interesting to note that the strong SST cold bias observed over west Pacific and west Atlantic sub-tropical gyre regions (Figure 9(a)) is completely absent in modified microphysics run (Figure 9(c)). Tomita et al. (2013) have shown that there is a strong feedback between clouds and Kuroshio front. This reduction in SST suggests that the cold bias in CTL run mostly due to the atmospheric processes. We also note that most of the enhancement in SST bias comes from the modified cloud microphysics as when both CRHvari and modified cloud microphysics are included, the SST bias (Figure 9(d)) remains almost same as the case with modified cloud microphysics alone (Figure 9(c)). The warmer SST may trigger enhanced convection over the Indian Ocean (IO) basin, which could have a positive impact on the model s performance of monsoon simulation which is discussed in following sections. The large warm bias in SST simulations in the north Pacific is a cause of concern, and we plan to address this issue in a subsequent study. The large change of SST is most likely due to change in net heat flux at surface (Qnet) over that region. How the change in microphysics parameterization led to this change in Qnet will be analysed Lower and upper tropospheric circulations features The south Asian monsoon is characterized by a cross-equatorial flow leading to the low level jet (the Findlater Jet) at 850 hpa (Figure 10(a); NCEP). The

11 REVISED CLOUD MICROPHYSICAL SCHEME IN CFSV2 FOR SIMULATION OF ISMR Figure 9. JJAS mean sea surface temperature (SST) bias (in C). (a) CTL minus Reynolds, (b) CRHvari minus Reynolds, (c) modified microphysics (revzc) minus Reynolds and (d) modified microphysics plus CRH (revzc + CRHvari) minus Reynolds. maximum wind magnitude at 850 hpa reaches 16 ms 1 or more in the south-west Arabian Sea. The unique feature in the upper atmosphere 200 hpa is the large anticylonic circulation with centre around 70 E and 30 N leading to the tropical easterly jet (TEJ) with its centre around 75 E and 10 N (Figure 10(b)). The TEJ maximum is related to the strength of the anticyclone. The large-scale low level wind features are reasonably well captured in the CTL simulation (Figure 10(c)) with the exception of an underestimation of the easterly winds over the south equatorial central IO. However, the upper level anticyclone is significantly underestimated in the CTL leading to a significant underestimation of the TEJ maximum by more than 10 m s 1 (Figure 10(d)). The variable critical humidity (CRHvari) threshold for large-scale convection is neither able to significantly change the low level winds nor could contribute to the improvement of the simulation of the upper level anticyclone and the TEJ (Figure 10(e) and (f)). This conclusion is valid even for the other experiment with a uniform higher critical humidity threshold (CRH90). Upper tropospheric circulation (at 200 hpa) is much better reflected in modified microphysics with CRHvari experiment (revzc + CRHvari) and is considerably improved as compared to CTL run (Figure 11(b)). TEJ over the southern India and adjoining equatorial IO (at 10 N) is well depicted by revzc + CRHvari experiment. Improvement in wind magnitude is noted in TEJ regions as compared to CTL run (Figure 10(d)). The low level circulation including the low level Jet stream is also well simulated as thermal gradients are well captured (Figure 11(a)). The strengthening of the TEJ hints at strong monsoon (reduced dry bias) as compared to the CTL. Overall, upper and lower tropospheric circulation patterns are much improved in the modified microphysics plus CRHvari experiment (Figure 11(c) and (d)) which is expected to have positive impact on rainfall (described in Section 3.5). In a nutshell, therefore, we may conclude that the modified cloud microphysics simulates better cloud properties (e.g. cloud fraction and vertical structure of cloud condensate) leading to better simulation of temperature distribution and the heat source for ISM, and finally the associated atmospheric circulation Rainfall distribution Seasonal mean rainfall over the south Asian monsoon region is characterized by two lines like maxima one along the Western Ghats and the other along the Myanmar coast extending to the north-east India (Figure 12(a), observation from GPCP). There is also significant amount of precipitation over the central India together with a secondary maximum over the equatorial eastern IO (Figure 12(a)). Model CTL experiment is able to replicate rainfall patterns over the Western Ghats and north-east India (Figure 12(b)). However, it underestimates the rainfall over central India and overestimates in the equatorial eastern IO leading to

12 A. HAZRA et al. Figure 10. (a) The Monsoon low level circulations (at 850 hpa) and (b) monsoon upper level wind patterns (at 200 hpa) from NCEP are shown. Then, for control run (CTL) in CFSv2, (c) low level circulations (at 850 hpa) and (d) upper level wind patterns (at 200 hpa) are presented. Finally, CRHvari sensitivity experiment in CFSv2, low level circulations (at 850 hpa) and upper level wind patterns (at 200 hpa) are shown in (e) and (f), respectively. Figure 11. The wind patterns for CFSv2 with modified microphysics with CRHvari (revzc + CRHvari) sensitivity experiment are presented. (a) The Monsoon low level circulations (at 850 hpa) and (b) upper level wind patterns (at 200 hpa). The wind speed differences at 850 and 200 hpa (revzc + CRHvari minus CTL) are also presented in the panels (c) and (d), respectively.

13 REVISED CLOUD MICROPHYSICAL SCHEME IN CFSV2 FOR SIMULATION OF ISMR Figure 12. JJAS mean rainfall patterns are presented: (a) observation (GPCP), (b) control (CTL), (c) sensitivity experiment with CRHvari, (d) experiment with modified microphysics (revzc) and (e) experiment with modified microphysics plus CRH (revzc + CRHvari). The precipitation difference is also shown: (f) CRHvari minus CTL, (g) revzc minus CTL and (h) revzc + CRHvari minus CTL. a dry bias (Figure 13(a)) over the continent and wet bias over eastern equatorial IO (Saha et al., 2014a). Over the continent, the wet region does not extend sufficiently to the western part of India. This may be related to excessive dry air intrusion from the western desert region to central India (Figure 10(a) and (c)). JJAS precipitation simulated by CRHvari (Figure 12(c)) remains very similar to that by CTL (Figure 12(b)) with the bias (Figure 13(b)) also being quite similar to that in the CTL (Figure 13(a)). However, there is some development in the dry bias over Indian landmass as well as the wet bias over the equatorial eastern IO (Figure 12(f)). While RHcrit plays a role in modifying the clouds and its properties which in-turn modifies the dynamic characteristics of troposphere, we find that it influences the precipitation over the south Asian region only marginally. As discussed in the introduction, there could be a greater role of other microphysical processes which governs precipitation, e.g. modification of cloud parameterization particularly by modifying tendency equations of rain and snow auto-conversions rates. The model with only modified microphysics (revzc) is indeed able to represent basic features of seasonal mean rainfall better (Figure 12(d)) with significant progress of the dry bias over the continent (Figure 13(c)). This improvement is clearly seen

14 A. HAZRA et al. Figure 13. JJAS precipitation-bias (model minus observation) for (a) control experiment (CTL) and other three sensitivity experiments: (b) sensitivity experiment with CRHvari, (c) experiment with modified microphysics (revzc) and (d) experiment with modified microphysics plus CRHvari (revzc + CRHvari). in the difference between JJAS precipitation simulated by the modified microphysics run and that simulated by the CTL run (Figure 12(g)). Similarly, modified microphysics plus CRHvari (revzc + CRHvari) is also able to represent the seasonal mean monsoon rainfall pattern well (Figure 12(e)) and the development visibly seen in the difference between modified microphysics with CRHvari run and the CTL run (Figure 12(h)). The systematic reduction of dry bias over the south Asian monsoon region with addition of CRHvari to modified cloud microphysics is shown in Figure 13(d). The combined effect of the two modifications not only improves the dry bias over the Indian continent but also reduces the wet bias in the eastern IO to close to observation (Figure 13(d)). However, it adds a region of wet bias to the western equatorial IO. Over central China, it enhances the already existing wet bias in CFSv.2 (Figure13(a)) by a significant amount (Figure 13(c)). This also remains a concern that we will have to address later. The annual cycle of all India averaged rainfall (B1, E and N) is presented in Figure 14(a). It shows significant reduction of the dry bias in this present modification (Figure 14(a)). The mean bias during JJAS has come down from 30% in the CTL simulations to about 18% in the case of revzc + CRHvari simulations. It is interesting to note that the rainfall is closer to observation, especially in the last phase of monsoon (during withdrawal phase) (Figure 14(a)). The south Asian monsoon system also involves rainfall over the north Bay of Bengal, and hence Goswami et al. (1999) defined rainfall over this region as Extended Indian Monsoon Rainfall (EIMR) index. The south Asian monsoon rainfall, however, is not confined to only over the Indian continent and needs to be defined over a slightly larger region (Goswami et al., 1999) namely in the box B2 (70 95 E and N). This box basically represents the south Asian monsoon rainfall and is used by number of previous researcher to access the performance of Indian monsoon (Goswami et al., 1999). The CTL simulation still has large dry bias even over this region ( 11% reduction compared to observation during JJAS, Figure 14(b)). It is remarkable that the annual cycle of precipitation over this region simulated by revzc + CRHvari matches almost identically with that of observations. The simulated annual cycle also indicates that the onset and withdrawal of the south Asian monsoon are well simulated, consistent with better simulation of ΔTT (Figure 8(c)). This is considered a major improvement in the simulation of the Asian monsoon by the model. Does the modified cloud microphysics improves the bias in simulating too much convective precipitation (Saha et al., 2014a) and too little stratiform precipitation? The ratio of convective to total precipitation over the Asian monsoon region simulated by revzc + CRHvari experiment (Figure 14(c)) indicates that there is some progress over the land region. Averaged over the Indian continent, this improvement is confined to the early part of the south Asian monsoon (May July) with no significant improvement during August September (Figure 14(d)). The clue for the convective and total rain ratio coming closer to Tropical Rainfall Measuring Mission (TRMM) only during May July can be attributed to the model microphysics processes (Hazra et al., 2014). During these months, total

15 REVISED CLOUD MICROPHYSICAL SCHEME IN CFSV2 FOR SIMULATION OF ISMR Figure 14. (a) Annual cycle of all India (AI; longitude: E, latitude: N) averaged precipitation (mm day 1 ) for GPCP (thick black line), CTL (thin blue line with cross symbol), and revzc + CRHvari (thin red line with plus symbol), and (b) similar annual cycle (averaged over longitude: E, latitude: N) of precipitation (mm day 1 ) for GPCP, CTL, revzc and revzc + CRHvari. (c) The spatial distribution of convective to total rainfall ratio for the experiment with modified microphysics plus CRHvari (revzc + CRHvari). (d) Monthly variation of convective to total rainfall ratio (averaged over E and N) for control (CTL), revzc + CRHvari, TRMM. rain bifurcates almost equally into convective and stratiform rain, thus rain ratio (convective to total) is realistic. However, during other seasons, this microphysical rain is very weak as compared to convective rain and thus the simulated rain ratio (convective to total) differs from the observed values. The other interesting point is that this development of dry bias is not limited over only Indian subcontinent alone. Other regions of global tropics especially dry bias over continental regions of central Africa and northern South America are also improved significantly (Figure 15). While some biases in tropical precipitation such as the shift of the precipitation band (ITCZ) in Pacific and Atlantic still exist, the modified/revised cloud scheme clearly represents an realistic simulation of the ISM by a better portrayal of temperature distribution (Figures 7(d) and 8(a) and (b)) and betterment in precipitation formation in the tropics with dry biases reduced over different parts of global tropical regions (Figure 15). Precipitation overestimation is seen over Western coast of South America. On the other hand, dry and wet biases appear in the equatorial Pacific region (for example in the Pacific warm pool). 4. Summary and conclusions A dry bias in simulating JJAS precipitation over the south Asian monsoon region is a generic problem in most of the climate models (Kumar et al., 2005; Kripalani et al., 2007; Rajeevan and Nanjundiah, 2009) and limits the skill of predicting the south Asian monsoon. Hence, improving this dry bias in climate models is a challenging scientific problem with great social relevance. This study attempts to make a contribution to climate modelling in general by trying to reduce the persistent dry rainfall bias over the Indian subcontinent in the context of the CFSv2 model. It is argued that the dry bias over the Indian land mass is linked with other concomitant model biases of the model simulations such as the cold TT bias and cold SST bias. For improvement of the ISM precipitation in the CFSv2 model, the following two hypothesis are tested (1) Better simulation of precipitation through improved TT simulation via improved climatological representation of clouds by physically based adjustment of RHcrit and (2) Increase of TT through modification of cloud scheme in the model, specially alteration of the tendency equations of auto-conversion rates and addition of rain accretion rate. To testify first hypothesis, two sets of sensitivity experiments along with the CTL experiment are performed by tuning RHcrit to 90%, using vertically variable values of RHcrit and default value of 85% for the CTL experiment, respectively. The CRHvari experiment is closer to the observed natural values over the study domain as compared to the CRH90 and CTL run. The mean biases in several model parameters governing the ISM strength [as noted in previous studies, e.g. Pokhrel et al. (2012a) and Saha et al. (2014a)] are reduced. Middle and low cloud fraction with RHcrit (CRH90 and CRHvari) experiments decreases marginally or remains constant. The present

16 A. HAZRA et al. Figure 15. JJAS mean precipitation-bias (model minus observation) for different sensitivity experiments over global tropical region: (a) CTL minus GPCP, (b) CRHvari minus GPCP, (c) modified microphysics (revzc) minus GPCP and (d) modified microphysics plus CRHvari (revzc + CRHvari) minus GPCP. sensitivity studies also reveal that the underestimation of high cloud cover in CFSv2 CTL run is now improved in CRH tuning experiment. Thus, we may conclude that CRH tuning is one way to correct the cloud biases particularly high level cloud fraction over the tropics. Although model still suffers from cold TT bias in CRH tuning experiments but as compared to the CTL run, its severity is reduced and its values suggest relatively strong monsoon with reduced dry precipitation bias, but this improvement is marginal over central India. On the similar lines, the cold SST bias too is mitigated to some extent, and instead of it warm SST bias over equatorial and eastern IO basin has emerged. To validate the second hypothesis, another sensitivity experiment with modified/revised microphysics scheme and with better RHcrit (i.e. CRHvari) is performed. In contrast to only CRHvari experiments, modified cloud microphysical scheme with CRHvari clearly indicates the improvement of precipitation formation and significant reduction of dry biases not only over the continental India but also in other parts of global tropical regions such as the central Africa and Amazonia. Improvement of ΔTT over the south Asian monsoon region is also striking which actually helps to increase the rainfall over Indian land mass. The improvements related to cloud cover, cloud condensate are also significant. The SST, TT and monsoon circulation patterns are also improved noticeably. As a result of near perfect simulation of ΔTT over the region, the onset and the withdrawal of the south Asian monsoon are also well simulated by the model with modified cloud microphysics. Thus, the representation of microphysical processes (auto-conversion and accretion) in the warm phase and mixed-phase cloud of a CFSv2 is vital for the realistic depiction of cloud and precipitation production, and consequently better representation of ISM. The modified/revised cloud microphysics (revzc) is important to get substantially better high and mid cloud distribution (Figure 4) which leads to modify the vertical profile of temperature simulations (Figure 8) and the north south gradient of temperature over the south Asian monsoon region (Figure 7(d)). Further in-depth studies are also required for the convective parameterization, radiative properties and cloud-radiation feedbacks. We also realize that for future improvement of biases of ISMR may require improvement of the convective parameterization as well. We are pursuing this problem, and results will be reported in the forthcoming study. Acknowledgement Authors are thankful to Director, Indian Institute of Tropical Meteorology (IITM), for providing encouragement to carry out this research work. The authors acknowledge the Ministry of Earth Sciences, Government of India, for the sanction of the Monsoon Mission research project. Authors are thankful to Dr Moorthi and Dr Hou for

17 REVISED CLOUD MICROPHYSICAL SCHEME IN CFSV2 FOR SIMULATION OF ISMR providing suggestions. Authors duly acknowledge Dr X. Jiang, JPL for providing CloudSat data and acknowledge CALIPSO, NCEP and MERRA dataset. Authors also thank two anonymous reviewers and editor for their constructive comments and suggestions. References Abhik S, Halder M, Mukhopadhyay P, Jiang X, Goswami BN A possible new mechanism for northward propagation of boreal summer intraseasonal oscillations based on TRMM and MERRA reanalysis. Clim. Dyn. 40: , doi: /s x. Abhilash S, Sahai AK, Pattnaik S, Goswami BN, Kumar A Extended range prediction of active-break spells of Indian summer monsoon rainfall using an ensemble prediction system in NCEP Climate Forecast System. Int. J. 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