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Indian Summer Monsoon (ISM) and East Asian Summer Monsoon (EASM) are the most important weather systems in India and Korea respectively. The interannual variations of ISM and EASM largely depend on the anomalies of sea surface temperatures (SSTs) of Tropical Equtorial Pacific and Indian Ocean and also on the Eurasian Snow Depth/Cover. In this study, the relative roles of these two most important surface boundary conditions on the Indian and Korean monsoons have been examined using the high resolution (40 km) global numerical weather prediction model of German Weather Service (GME). Four sets of experiments were carried out by varying the climatological and observed values of SSTs and snow. This study shows that there is a negative (positive) relationship between the Western (Eastern) Eurasian snow depth and ISMR, whereas a positive (negative) relationship between the Western (Eastern) Eurasian snow depth and Korean monsoon rainfall has been observed. Results show that when observed SST and snow are prescribed to the model as boundary conditions, the simulated winds and rainfall are close to the NCEP/NCAR reanalyzed winds and GPCP rainfall, respectively. This study reveals that the model simulated summer monsoons in Indian and Korean domains are not good enough unless an observed Eurasian snow is prescribed in addition to the observed SST. Key Points: Relative roles of SSTs of Tropical Pacific and Indian Ocean and Eurasian snow on the Indian and Korean monsoons have been examined at 40 km resolution using GMEA negative (positive) relationship between the Western (Eastern) Eurasian snow depth and ISMR has been foundA positive(negative) relationship between the Western(Eastern) Eurasian snow depth and Korean monsoon rainfall has been observed [ABSTRACT FROM AUTHOR]

The regional climate model version 4 (RegCM4) is analyzed in this study to assess Indian summer monsoon precipitation (ISMP) over six homogeneous precipitation regions and various meteorological subdivisions of India embedded therein during a reference period (1976–2005) and mid- (2031–2060) and far-future (2070–2099) periods under the RCP8.5 scenario over the South Asia CORDEX domain. A Coupled Model Intercomparison Project (CIMIP5) global model GFDL-ESM2M provides initial and boundary conditions to the RegCM4 under the high-emission scenario RCP8.5. RegCM4 precipitation fields are validated against observed India Meteorological Department (IMD) and Asian Precipitation-Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE) precipitation datasets, while wind and specific humidity fields obtained from RegCM4 are validated against National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and the parent GFDL model integrated fields. Model comparisons indicate that RegCM4 captures the regional characteristics of ISMP satisfactorily in terms of biases, trends, interannual variability, and circulation patterns. RegCM4 precipitation fields show high correlations of 0.9 and 0.8 with those of IMD and APHRODITE, respectively, and RegCM shows better skill in comparison with GFDL over about 68% of meteorological subdivisions. RegCM4 projects increases in precipitation by about 15.3% (28.4%), 5.1% (16.2%), and 5.4% (18.4%) respectively over the Northwest (NW), Northeast (NE) and Hilly Regions (HR) in the mid-(far-)future but decreases in precipitation over the Peninsular (PE) region by about −1.5% (−15.7%) during the same period. A similar precipitation change pattern is also found when analyzing the probability distribution functions (PDFs) over the same regions. The precipitation intensity (95th percentile) shows increases above 40% over numerous subdivisions of the West Central (WC), NW, and HR regions. The present analysis also reveals significant increases of more than 50% in mean precipitation over several meteorological subdivisions. Analysis of the circulation fields depicts a northward shift of the high-precipitation belt while high-pressure systems dominate the peninsular region when approaching the central India global warming scenario. It is interesting to note that the extreme precipitation index Rx5day exactly follows the pattern of projected increase in mean precipitation. In addition, it is noted that the projected variability and change in the mean precipitation are less frequent than for RX5day, while a consistently stronger spread in variability is projected in the mid- to far-future under the warming scenario. [ABSTRACT FROM AUTHOR]

This study assess the performance of two versions of Regional Climate Model (RegCM) in simulating the Indian summer monsoon over South Asia for the period 1998 to 2003 with an aim of conducting future climate change simulations. Two sets of experiments were carried out with two different versions of RegCM (viz. RegCM4.2 and RegCM4.3) with the lateral boundary forcings provided from European Center for Medium Range Weather Forecast Reanalysis (ERA-interim) at 50 km horizontal resolution. The major updates in RegCM4.3 in comparison to the older version RegCM4.2 are the inclusion of measured solar irradiance in place of hardcoded solar constant and additional layers in the stratosphere. The analysis shows that the Indian summer monsoon rainfall, moisture flux and surface net downward shortwave flux are better represented in RegCM4.3 than that in the RegCM4.2 simulations. Excessive moisture flux in the RegCM4.2 simulation over the northern Arabian Sea and Peninsular India resulted in an overestimation of rainfall over the Western Ghats, Peninsular region as a result of which the all India rainfall has been overestimated. RegCM4.3 has performed well over India as a whole as well as its four rainfall homogenous zones in reproducing the mean monsoon rainfall and inter-annual variation of rainfall. Further, the monsoon onset, low-level Somali Jet and the upper level tropical easterly jet are better represented in the RegCM4.3 than RegCM4.2. Thus, RegCM4.3 has performed better in simulating the mean summer monsoon circulation over the South Asia. Hence, RegCM4.3 may be used to study the future climate change over the South Asia. [ABSTRACT FROM AUTHOR]

This study is aimed at examining the consistency of the relationship between load of dust and rainfall simulated by different climate models and its implication for the Indian summer monsoon system. Monthly mean outputs of 12 climate models, obtained from the archive of the Coupled Model Intercomparison Project phase 5 (CMIP5) for the period 1951-2004, are analyzed to investigate the relationship between dust and rainfall. Comparative analysis of the model simulated precipitation with the India Meteorological Department (IMD) gridded rainfall, CRU TS3.21 and GPCP version 2.2 data sets show significant differences between the spatial patterns of JJAS rainfall as well as annual cycle of rainfall simulated by various models and observations. Similarly, significant inter-model differences are also noted in the simulation of load of dust, nevertheless it is further noted that most of the CMIP5 models are able to capture the major dust sources across the study region. Although the scatter plot analysis and the lead-lag pattern correlation between the dust load and the rainfall show strong relationship between the dust load over distant sources and the rainfall in the South Asian region in individual models, the temporal scale of this association indicates large differences amongst the models. Our results caution that it would be pre-mature to draw any robust conclusions on the time scale of the relationship between dust and the rainfall in the South Asian region based on either CMIP5 results or limited number of previous studies. Hence, we would like to emphasize upon the fact that any conclusions drawn on the relationship between the dust load and the South Asian rainfall using model simulation is highly dependent on the degree of complexity incorporated in those models such as the representation of aerosol life cycle, their interaction with clouds, precipitation and other components of the climate system. [ABSTRACT FROM AUTHOR]

ABSTRACT A general circulation model (GCM) is an alternative way for predicting Indian summer monsoon rainfall (ISMR) over the existing empirical/statistical models in recent time. However, the inherent biases present in the GCM affect its performance. Therefore, there is a high requirement for bias correction of the GCM. Few studies on bias correction of GCMs are available in the context of ISMR. A comparative study is reported in this paper on the six different bias correction methods by applying on the hindcast (May start, June-July-August-September) of the climate forecast system (CFS) model from the National Centers for Environmental Prediction (NCEP) for 27 years (1982-2008). Among the six methods discussed in this paper, three methods did not use any statistical transformation (Mean Bias-remove technique (U), Multiplicative shift technique (M) and Standardized-reconstruction technique (Z)) and the remaining three methods used statistical transformation (Regression technique (R), Quantile Mapping Method (Q), Principal Component Regression (PCR)). Finally, it was found that the Standardized-reconstruction technique (Z) and Quantile Mapping Method (Q) are more skilful than the others and both are equally skilful in simulating ISMR. Bias-corrected rainfall in four extreme years, out of which 1988 and 1994 are characterized by excess rainfall and 1987 and 2002 are characterized by deficit, are also examined here. Results indicate that both methods efficiently capture the extreme rainfall cases. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, canonical correlation analysis (CCA) has been used to statistically downscale the seasonal predictions of the Indian summer monsoon rainfall (ISMR) from a global spectral model. An extensive diagnostic study of the global model products and observed data for the period 1981-2008 indicates that while the predictions of rainfall anomalies have poor skill, the mean flow patterns are brought out reasonably well by the model. The model precipitation is found to be more strongly dependent on sea surface temperature over the Nino regions in the Pacific Ocean. However, the observed precipitation has a stronger links to winds at 850 hPa near the Somali coast than is evident in the model. On the basis of correlation maps, potential model predictors (specific humidity and zonal and meridional winds over different regions at different levels) are chosen for CCA for the prediction of ISMR. Using leave-three-out cross-validation technique, canonical coefficients are computed using 25 years data (as training period) for CCA model. With this, predictions from the CCA model have also been prepared for the period of 1981-2005 to evaluate the performance. In addition to the above, predictions are made for four independent years (2006-2009). An improvement in skill of the composite forecasts (obtained using all the predictors) in terms of interannual variability is noticed over some parts of east- and northeast India as well as many parts of peninsular region especially over west coast of India. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The article discusses various factors and conditions contributing to the climatic changes, wind movement and representation of orography in a spectral general circulation model (GCM). An environmental study is conducted to compare the two aspects of the representation of orography as well as the seven types of digital filters. Digital filtering is considered the main approach in the analysis and representation of orography. Moreover, the study show that two dimensional Lanczos filter is the most effective in reducing the ripples and the associated errors in the orographic representation.

The Regional Climate Model RegCM3 has been used to examine its suitability in simulating the Indian summer monsoon circulation features and associated rainfall. The model is integrated at 55 km horizontal resolution over a South Asia domain for the period April–September of the years 1993 to 1996. The characteristics of wind at 850 hPa and 200 hPa, temperature at 500 hPa, surface pressure and rainfall simulated by the model over the Indian region are examined for two convective schemes (a Kuo-type and a mass flux scheme). The monsoon circulation features simulated by RegCM3 are compared with those of the NCEP/NCAR reanalysis and the simulated rainfall is validated against observations from the Global Precipitation Climatology Centre (GPCC) and the India Meteorological Department (IMD). Validation of the wind and temperature fields shows that the use of the Grell convection scheme yields results close to the NCEP/NCAR reanalysis. Similarly, the Indian Summer Monsoon Rainfall (ISMR) simulated by the model with the Grell convection scheme is close to the corresponding observed values. In order to test the model response to land surface changes such as the Tibetan snow depth, a sensitivity study has also been conducted. For such sensitivity experiment, NIMBUS-7 SMMR snow depth data in spring are used as initial conditions in the RegCM3. Preliminary results indicate that RegCM3 is very much sensitive to Tibetan snow. The model simulated Indian summer monsoon circulation becomes weaker and the associated rainfall is reduced by about 30% with the introduction of 10 cm of snow over the Tibetan region in the month of April. [ABSTRACT FROM AUTHOR]

Many studies based on observed data indicate the inverse relationship between the Eurasian snow cover/depth and the Indian summer monsoon rainfall (ISMR). The purpose of this study is to confirm the inverse snow-ISMR relationship by using the observed snow depth data as boundary conditions in the spectral general circulation model (GCM) of Indian Institute of Technology, Delhi (IITD), and to examine the influence of Eurasian snow depth on the monsoon circulation. The original model belonging to the European Centre for Medium range Weather Forecasts (ECMWF) at resolution T21 has been modified extensively to a higher resolution of T80L18 at IITD. A two-dimensional Lanczos digital filter has been used to represent the orography realistically. The Historical Soviet Daily Snow Depth (HSDSD) version II data set has been used for conducting sensitivity experiments using the above model. Two sensitivity experiments have been designed, corresponding to two contrasting cases: one with high Eurasian snow depth in spring followed by deficient ISMR and the second with low snow depth followed by excess ISMR. The difference fields of mean monsoon circulation simulated in the above two experiments are examined in detail in order to confirm the influence of Eurasian snow depth on ISMR and to examine the Asian summer monsoon circulation and rainfall. Copyright © 2006 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

In this paper, the relationship between seasonal mean (June, July, August and September) monsoon circulation features and the midlatitude circulations in winter and spring seasons have been examined during contrasting years of more (less) number of snow days in winter/spring followed by deficient (excess) Indian Summer Monsoon Rainfall (ISMR) using NCEP/NCAR reanalyzed data for the period 1966-1994. The Historical Soviet Daily Snow Depth (HSDSD) version II data set has been used to calculate the number of days of snow over west and east Eurasia separately under three classes: class 1 for SD > 5 cm, class 2 for SD > 10cm and class 3 for SD > 50 cm where SD stands for snow depth. Correlation coefficients are computed between the anomaly in the number of days of snow depth under the above three classes during winter/spring over west and east Eurasia and the subsequent ISMR. HSDSD data show that difference in the number of days of SD > 10 cm in two extreme years is most prominent in the west Eurasia in the months of January and April. Also the anomaly in the number of days of snow in January and April over west Eurasia has correlation coefficients of -0.69 and -0.56 with the following ISMR, respectively at 0.1% significance level when the SD is more than 10 cm at all the stations. Results also show that low-level atmospheric temperature difference between two extreme years of snow days in winter is up to 10°C and the cooling persists up to spring season with a difference of 2 °C. This cooling persistence may give rise to anomalous cyclonic circulations over the midlatitudes and tropics which may be responsible for weakening the monsoon circulation over India during the year of more snow days over west Eurasia. [ABSTRACT FROM AUTHOR]

The variability of Indian summer monsoon rainfall (ISMR; both at interannual as well as intraseasonal timescales) has a direct impact on various sectors of public interest and economy such as agriculture, water resource, etc. So, the need of real-time extended range forecast system of the ISMR (monthly to seasonal scale) is not overstated. The present study is aimed at developing such a forecast system to predict rainfall (monthly and seasonal mean) one month in advance over 34 meteorological subdivisions of India for climate risk management in agriculture. Three different statistical approaches, viz. singular value decomposition-based multiple regression, supervised principal component regression and canonical correlation analysis are applied on rainfall products obtained from eight global models. The hindcast (1982-2010) skills of all the three methods are found to be better than the simple ensemble mean of all models for all- India level. However, a large variation in the skill of each of the techniques is noticed when studied over smaller regions, i.e. at meteorological subdivision level. Therefore, a combination of all the three methods has been developed for real-time experimental forecast of rainfall. In order to incorporate uncertainties in the predictions, the final forecast is also prepared as probability values. These extended range predictions have been evaluated for the monsoon-2011 against India Meteorological Department observed rainfall. It is found that the extended range experimental forecasts of rainfall are usable for more than 50% of the meteorological subdivisions as well as the whole of India at monthly and seasonal scale. [ABSTRACT FROM AUTHOR]