An Indian monsoon intraseasonal oscillations (MISO) index for real time monitoring and forecast verification

The wet/dry spells of the Indian summer monsoon (ISM) rainfall are governed by northward propagating boreal summer monsoon intraseasonal oscillations (MISO). Unlike for the Madden Julian Oscillation (e.g. RMM indices, Wheeler and Hendon in Mon Weather Rev 132:1917–1932, 2004), a low dimensional real-time monitoring and forecast verification metric for the MISO is not currently available. Here, for the first time, we present a real time monitoring index developed for identifying the amplitude and phase of the MISO over the ISM domain. The index is constructed by applying extended empirical orthogonal function (EEOF) analysis on daily unfiltered rainfall anomalies averaged over the longitudinal domain 60.5°E–95.5°E. The gravest two modes of the EEOFs together explain about 23 % of the total variance, similar to the variance explained by MISO in observation. The pair of first two principal components (PCs) of the EEOFs is named as MISO1 and MISO2 indices which together represent the evolution of the MISOs in a low dimensional phase space. Power spectral analysis reveals that the MISO indices neatly isolate the MISO signal from the higher frequency noise. It is found that the current amplitude and phase of the MISO can be estimated by preserving a memory of at least 15 days. Composite pictures of the spatio-temporal evolution of the MISOs over the ISM domain are brought out using the MISO indices. It is further demonstrated that the MISO indices can be used in the quantification of skill of extended range forecasts of MISOs. Since the MISO index does not rely on any sort of time filtering, it has great potential for real time monitoring of the MISO and may be useful in developing some prediction scheme.     

Prediction of Indian summer monsoon in short to medium range time scale with high resolution global forecast system (GFS) T574 and T382

Performance of national centers for environmental prediction based global forecast system (GFS) T574/L64 and GFS T382/L64 over Indian region has been evaluated for the summer monsoon season of 2011. The real-time model outputs are generated daily at India Meteorological Department, New Delhi for the forecasts up to 7 days. Verification of rainfall forecasts has been carried out against observed rainfall analysis. Performance of the model is also examined in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water content. Case study of a monsoon depression is also illustrated. Results obtained show that, in general, both the GFS T382 and T574 forecasts are skillful to capture climatologically heavy rainfall regions. However, the accuracy in prediction of location and magnitude of rainfall fluctuates considerably. The verification results, at the spatial scale of 50 km resolution, in a regional spatial scale and country as a whole, in terms of continuous skill score, time series and categorical statistics, have demonstrated superiority of GFS T574 against T382 over Indian region. Both the model shows bias of lower tropospheric drying and upper tropospheric moistening. A bias of anti-cyclonic circulation in the lower tropospheric level lay over the central India, where rainfall as well as precipitable water content shows negative bias. Considerable differences between GFS T574 and T382 are noticed in the structure of model bias in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water contents. The magnitude of error for these parameters increases with forecast lead time in both GFS T574 and T382. The results documented are expected to be useful to the forecasters, monsoon researchers and modeling community.     

Meteorological subdivisional-level extended range forecast over India during southwest monsoon 2012

The real-time multi-model ensemble (MME)-based extended range (up to 3 weeks) forecast of monsoon rainfall over India during the 2012 monsoon season is analyzed using the outputs of European Centre for Medium Range Weather Forecasts (ECMWF) monthly forecast coupled model, National Centre for Environmental Prediction (NCEP) Climate Forecast System version 2 coupled model and Japan Meteorological Agency (JMA)’ ensemble prediction system. Although the individual models show useful skill in predicting the extended range forecast of monsoon, the MME forecast is found to be superior compared to these. For the country as a whole, the correlation coefficient (CC) between the observed and MME forecast rainfall departure is found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days). Over the four homogeneous regions of India, the CC is found to be significant (above 95 % level) up to 2 weeks except in case of northeast India, which shows significant CC for week 1 (days 5–11) only. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the mean percentage of correct forecast is found to be much higher than the climatology forecast. Considering the complex problem of forecasting of monsoon in the extended range timescales, the MME-based predictions for 2–3 weeks provide skillful results and useful guidance for application in agriculture and other sectors in India.     

Active and break phases of the South American summer monsoon: MJO influence and subseasonal prediction

The role of the Madden–Julian Oscillation (MJO) in producing active and break periods of the South American (SA) monsoon and the performance of the ECMWF and NCEP models in predicting these periods at multiweek lead times are assessed. Two monsoon indices, based on precipitation and wind, are proposed to characterize these periods. The models represent well the observed association of active and break monsoon days with large scale convection and circulation anomalies. Although reproducing approximately the distribution of active and break days proportions in each phase of the MJO cycle, models produce a phase shift between observed and simulated distributions because they establish the teleconnection between Central Pacific and South America, as well as its impacts, sooner than in observations. The predictive skill of both rainfall and wind anomalies is limited to about 2 weeks, with the monsoon wind index displaying higher correlation score till week 3. The forecast performance is apparently not affected by initialization on active or break monsoon days. However, it is higher for prediction of lower precipitation in break days than heavier rainfall in active days. Wind is much better predicted than rainfall for active days, which could be used for extreme rainfall events forecast. Although relatively small at shorter lead times, the MJO contribution is the major source of rainfall predictability after week 3. To improve the multiweek prediction of SA monsoon, models need not only to predict correctly the MJO phase, but also to reproduce in the right phase the MJO-related SA rainfall anomalies.

     

Status of NCEP CFS vis-a-vis IPCC AR4 models for the simulation of Indian summer monsoon

National Centers for Environmental Prediction (NCEP) Coupled Forecast System (CFS) is selected to play a lead role for monsoon research (seasonal prediction, extended range prediction, climate prediction, etc.) in the ambitious Monsoon Mission project of Government of India. Thus, as a prerequisite, a detail analysis for the performance of NCEP CFS vis-a-vis IPCC AR4 models for the simulation of Indian summer monsoon (ISM) is attempted. It is found that the mean monsoon simulations by CFS in its long run are at par with the IPCC models. The spatial distribution of rainfall in the realm of Indian subcontinent augurs the better results for CFS as compared with the IPCC models. The major drawback of CFS is the bifurcation of rain types; it shows almost 80–90 % rain as convective, contrary to the observation where it is only 50–65 %; however, the same lacuna creeps in other models of IPCC as well. The only respite is that it realistically simulates the proper ratio of convective and stratiform rain over central and southern part of India. In case of local air–sea interaction, it outperforms other models. However, for monsoon teleconnections, it competes with the better models of the IPCC. This study gives us the confidence that CFS can be very well utilized for monsoon studies and can be safely used for the future development for reliable prediction system of ISM.     

Comparison of intra-seasonal forecast of Indian summer monsoon between two versions of NCEP coupled models

The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19–25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5–11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8–10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.     

Performance of the seasonal forecasting of the Asian summer monsoon by BCC_CSM1.1(m)

This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing Climate Center Climate System Model, BCC_CSM1.1(m). For the South and Southeast Asian summer monsoon, reasonable skill is found in the model's forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts. Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Niño3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and El Niño-Southern Oscillation.     

Application of multimodel ensemble techniques for real time district level rainfall forecasts in short range time scale over Indian region

During the summer monsoon (1 June to 30 September) 2007, real-time district level rainfall forecasts in short-range time scale were generated for Indian region applying multimodel ensemble technique. The pre-assigned grid point weights on the basis of correlation coefficients (CC) between the observed values and forecast values are determined for each constituent model at the resolution of 0.5° × 0.5° utilizing two seasons datasets (1 June to 30 September, 2005 and 2006), and the multimodel ensemble forecasts (day 1 and day 2 forecasts) are generated at the same resolution on a real-time basis. The ensemble forecast fields are then used to prepare forecasts for each district taking the average value of all grid points falling in a particular district. In this paper we examined the performance skill of the multimodel ensemble-based real-time district level short-range forecast of rainfall. It has clearly emerged from the results that the multimodel ensemble technique reported in this study is superior to each ensemble member. District wise performance of the ensemble rainfall forecast reveals that the technique, in general, is capable of providing reasonably good forecast skill over most districts of the country, particularly over the districts where the monsoon systems are dominant. Though the procedure shows appreciable skill to predict occurrence or non-occurrence of rainfall at the district level, it always underestimates rainfall amount, particularly in heavy rainfall events. Possible reasons of this failure may be due to model bias and poor data assimilation procedure.     

Influence of convective parameterization on the systematic errors of Climate Forecast System (CFS) model over the Indian monsoon region from an extended range forecast perspective

This study investigates the influence of Simplified Arakawa Schubert (SAS) and Relax Arakawa Schubert (RAS) cumulus parameterization schemes on coupled Climate Forecast System version.1 (CFS-1, T62L64) retrospective forecasts over Indian monsoon region from an extended range forecast perspective. The forecast data sets comprise 45 days of model integrations based on 31 different initial conditions at pentad intervals starting from 1 May to 28 September for the years 2001 to 2007. It is found that mean climatological features of Indian summer monsoon months (JJAS) are reasonably simulated by both the versions (i.e. SAS and RAS) of the model; however strong cross equatorial flow and excess stratiform rainfall are noted in RAS compared to SAS. Both the versions of the model overestimated apparent heat source and moisture sink compared to NCEP/NCAR reanalysis. The prognosis evaluation of daily forecast climatology reveals robust systematic warming (moistening) in RAS and cooling (drying) biases in SAS particularly at the middle and upper troposphere of the model respectively. Using error energy/variance and root mean square error methodology it is also established that major contribution to the model total error is coming from the systematic component of the model error. It is also found that the forecast error growth of temperature in RAS is less than that of SAS; however, the scenario is reversed for moisture errors, although the difference of moisture errors between these two forecasts is not very large compared to that of temperature errors. Broadly, it is found that both the versions of the model are underestimating (overestimating) the rainfall area and amount over the Indian land region (and neighborhood oceanic region). The rainfall forecast results at pentad interval exhibited that, SAS and RAS have good prediction skills over the Indian monsoon core zone and Arabian Sea. There is less excess rainfall particularly over oceanic region in RAS up to 30 days of forecast duration compared to SAS. It is also evident that systematic errors in the coverage area of excess rainfall over the eastern foothills of the Himalayas remains unchanged irrespective of cumulus parameterization and initial conditions. It is revealed that due to stronger moisture transport in RAS there is a robust amplification of moist static energy facilitating intense convective instability within the model and boosting the moisture supply from surface to the upper levels through convergence. Concurrently, moisture detrainment from cloud to environment at multiple levels from the spectrum of clouds in the RAS, leads to a large accumulation of moisture in the middle and upper troposphere of the model. This abundant moisture leads to large scale condensational heating through a simple cloud microphysics scheme. This intense upper level heating contributes to the warm bias and considerably increases in stratiform rainfall in RAS compared to SAS. In a nutshell, concerted and sustained support of moisture supply from the bottom as well as from the top in RAS is the crucial factor for having a warm temperature bias in RAS.     

Evaluation of the ENSEMBLES multi-model seasonal forecasts of Indian summer monsoon variability

The performance of the new multi-model seasonal prediction system developed in the frame work of the ENSEMBLES EU project for the seasonal forecasts of India summer monsoon variability is compared with the results from the previous EU project, DEMETER. We have considered the results of six participating ocean-atmosphere coupled models with 9 ensemble members each for the common period of 1960–2005 with May initial conditions. The ENSEMBLES multi-model ensemble (MME) results show systematic biases in the representation of mean monsoon seasonal rainfall over the Indian region, which are similar to that of DEMETER. The ENSEMBLES coupled models are characterized by an excessive oceanic forcing on the atmosphere over the equatorial Indian Ocean. The skill of the seasonal forecasts of Indian summer monsoon rainfall by the ENSEMBLES MME has however improved significantly compared to the DEMETER MME. Its performance in the drought years like 1972, 1974, 1982 and the excess year of 1961 was in particular better than the DEMETER MME. The ENSEMBLES MME could not capture the recent weakening of the ENSO-Indian monsoon relationship resulting in a decrease in the prediction skill compared to the “perfect model” skill during the recent years. The ENSEMBLES MME however correctly captures the north Atlantic-Indian monsoon teleconnections, which are independent of ENSO.     

Modelling of Indian monsoon rainfall series by univariate box—Jenkins type of models

The time domain approach, i.e. Autoregressive (AR) processes, of time series analysis is applied to the monsoon rainfall series of India and its two major regions, viz. North-West India and Central India. Since the original time series shows no modelable structure due to the presence of high interannual variability, a 3-point running filter is ap-plied before exploring and fitting appropriate stochastic models. Out of several parsimonious models fitted, AR(3) is found to be most suitable. The usefulness of this fitted model is validated on an independent datum of 18 years and some skill has been noted. These models therefore can be used for low skill higher lead time forecasts of monsoon. Further the forecasts produced through such models can be combined with other forecasts to increase the skill of monsoon forecasts.     

Spatio-temporal variability and predictability of summer monsoon onset over the Philippines

The spatio-temporal variability of boreal summer monsoon onset over the Philippines is studied through the analysis of daily rainfall data across a network of 76 gauges for the period 1977 to 2004 and the pentad Merged Analysis of Precipitation from the US Climate Prediction Center from 1979 to 2006. The onset date is defined using a local agronomic definition, namely the first wet day of a 5-day period receiving at least 40 mm without any 15-day dry spell receiving <5 mm in the 30 days following the start of that period. The onset is found to occur rather abruptly across the western Philippines around mid-May on average and is associated with the set-up of a “classical” monsoonal circulation with low-level easterlies subsequently veering to southerly, and then southwesterly. The onset manifests itself merely as a seasonal increase of rainfall over the eastern Philippines, where rainfall occurs throughout most of the year. Interannual variability of the onset date is shown to consist of a spatially coherent large-scale component, rather similar over the western and eastern Philippines, with a moderate to high amount of local-scale (i.e. station scale) noise. In consequence, the large-scale signal can be easily retrieved from any sample of at least 5–6 stations across the network although the local-scale coherence and fingerprint of the large-scale signal of the onset date are found to be stronger over the central Philippines, roughly from Southern Luzon to Northern Mindanao. The seasonal predictability of local onset is analyzed through a cross-validated canonical correlation analysis using tropical Pacific and Indian Ocean sea surface temperature in March and the 850 hPa May wind field from dynamical forecast models as predictors. The regional-scale onset, defined as the average of standardized local-scale anomalies in onset date, shows good predictive skill (r ≈ 0.8). Moreover, most of the stations show weak to moderate skill (median skill = 0.28–0.43 depending on the scheme) with spatial averaging across stations typically increasing skill to >0.6.     

Role of natural external forcing factors in modulating the Indian summer monsoon rainfall,the winter North Atlantic Oscillation and their relationship on inter-decadal timescale

We use reconstructed data and multi-centennial integrations performed with the Bergen Climate Model Version 2 to investigate the impact of natural external forcing factors on the Indian summer monsoon (ISM) rainfall, the winter North Atlantic Oscillation (NAO), and the potential relationship between the ISM rainfall and the winter NAO on decadal to inter-decadal timescales. The model simulations include a 600-year control integration (CTL600) and a 600-year integration with time-varied natural external forcing factors from 1400 to 1999 (EXT600). Both reconstructed data and the simulation showed increased ISM rainfall 2–3 years after strong volcanic eruptions. Strong volcanic eruptions decrease the Indian Ocean sea surface temperature (SST), which increases the strength of the southwesterly winds over the Arabian Sea. With negative externally-forced radiative anomaly, the lower stratospheric pole-to-equator winter temperature gradient is enhanced, leading to a positive winter NAO anomaly with a time lag of 1 year. There is no significant correlation between the winter NAO and ISM rainfall in CTL600. However, the ISM rainfall is significantly positively correlated with the winter NAO in EXT600, with the NAO leading by 2–4 years, which is consistent with the NAO–ISM rainfall relationship in the reconstructed data. We suggest that natural external forcing factors regulate the inter-decadal variability of both the winter NAO and the ISM rainfall and thus likely lead to an increased statistical but not causal relationship between them on the inter-decadal timescale over the past centuries.     

Prediction of summer monsoon rainfall over India using the NCEP climate forecast system

The performance of a dynamical seasonal forecast system is evaluated for the prediction of summer monsoon rainfall over the Indian region during June to September (JJAS). The evaluation is based on the National Centre for Environmental Prediction’s (NCEP) climate forecast system (CFS) initialized during March, April and May and integrated for a period of 9 months with a 15 ensemble members for 25 years period from 1981 to 2005. The CFS’s hindcast climatology during JJAS of March (lag-3), April (lag-2) and May (lag-1) initial conditions show mostly an identical pattern of rainfall similar to that of verification climatology with the rainfall maxima (one over the west-coast of India and the other over the head Bay of Bengal region) well simulated. The pattern correlation between verification and forecast climatology over the global tropics and Indian monsoon region (IMR) bounded by 50°E–110°E and 10°S–35°N shows significant correlation coefficient (CCs). The skill of simulation of broad scale monsoon circulation index (Webster and Yang; WY index) is quite good in the CFS with highly significant CC between the observed and predicted by the CFS from the March, April and May forecasts. High skill in forecasting El Nino event is also noted for the CFS March, April and May initial conditions, whereas, the skill of the simulation of Indian Ocean Dipole is poor and is basically due to the poor skill of prediction of sea surface temperature (SST) anomalies over the eastern equatorial Indian Ocean. Over the IMR the skill of monsoon rainfall forecast during JJAS as measured by the spatial Anomaly CC between forecast rainfall anomaly and the observed rainfall anomaly during 1991, 1994, 1997 and 1998 is high (almost of the order of 0.6), whereas, during the year 1982, 1984, 1985, 1987 and 1989 the ACC is only around 0.3. By using lower and upper tropospheric forecast winds during JJAS over the regions of significant CCs as predictors for the All India Summer Monsoon Rainfall (AISMR; only the land stations of India during JJAS), the predicted mean AISMR with March, April and May initial conditions is found to be well correlated with actual AISMR and is found to provide skillful prediction. Thus, the calibrated CFS forecast could be used as a better tool for the real time prediction of AISMR.     

DYNAMICAL PREDICTION OF WEST CHINA AUTUMN RAINFALL BY THE NCEP CLIMATE FORECAST SYSTEM

This study investigates the variation and prediction of the west China autumn rainfall (WCAR) and their associated atmospheric circulation features, focusing on the transitional stages of onset and demise of the WCAR. Output from the 45-day hindcast by the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) and several observational data sets are used. The onset of WCAR generally occurs at pentad 46 and decays at pentad 56, with heavy rainfall over the northwestern China and moderate rainfall over the south. Before that, southerly wind changes into southeasterly wind, accompanied by a westward expansion and intensification of the western Pacific subtropical high (WPSH), favoring rainfall over west China. On the other hand, during the decay of WCAR, a continental cold high develops and the WPSH weakens and shifts eastward, accompanied by a demise of southwest monsoon flow, leading to decay of rainfall over west China. The CFSv2 generally well captures the variation of WCAR owing to the high skill in capturing the associated atmospheric circulation, despite an overestimation of rainfall. This overestimation occurs at all time leads due to the overestimated low-level southerly wind. The CFSv2 can pinpoint the dates of onset and demise of WCAR at the leads up to 5 days and 40 days, respectively. The lower prediction skill for WCAR onset is due to the unrealistically predicted northerly wind anomaly over the lower branch of the Yangtze River and the underestimated movement of WPSH after lead time of 5 days.     

Forecasting quantitative rainfall over India using multi-model ensemble technique

A new approach to ensemble forecasting of rainfall over India based on daily outputs of four operational numerical weather prediction (NWP) models in the medium-range timescale (up to 5 days) is proposed in this study. Four global models, namely ECMWF, JMA, GFS and UKMO available on real-time basis at India Meteorological Department, New Delhi, are used simultaneously with adequate weights to obtain a multi-model ensemble (MME) technique. In this technique, weights for each NWP model at each grid point are assigned on the basis of unbiased mean absolute error between the bias-corrected forecast and observed rainfall time series of 366 daily data of 3 consecutive southwest monsoon periods (JJAS) of 2008, 2009 and 2010. Apart from MME, a simple ensemble mean (ENSM) forecast is also generated and experimented. The prediction skill of MME is examined against observed and corresponding outputs of each constituent model during monsoon 2011. The inter-comparison reveals that MME is able to provide more realistic forecast of rainfall over Indian monsoon region by taking the strength of each constituent model. It has been further found that the weighted MME technique has higher skill in predicting daily rainfall compared to ENSM and individual member models. RMSE is found to be lowest in MME forecasts both in magnitude and area coverage. This indicates that fluctuations of day-to-day errors are relatively less in the MME forecast. The inter-comparison of domain-averaged skill scores for different rainfall thresholds further clearly demonstrates that the MME algorithm improves slightly above the ENSM and member models.     

Indian summer monsoon rainfall variability in global coupled ocean-atmospheric models

Performance of seven fully coupled models in simulating Indian summer monsoon climatology as well as the inter-annual variability was assessed using multi member 1 month lead hindcasts made by several European climate groups as part of the program called Development of a European multi-model ensemble system for seasonal-to-inter-annual prediction (DEMETER). Dependency of the model simulated Indian summer monsoon rainfall and global sea surface temperatures on model formulation and initial conditions have been studied in detail using the nine ensemble member simulations of the seven different coupled ocean–atmosphere models participated in the DEMETER program. It was found that the skills of the monsoon predictions in these hindcasts are generally positive though they are very modest. Model simulations of India summer monsoon rainfall for the earlier period (1959–1979) are closer to the ‘perfect model’ (attainable) score but, large differences are observed between ‘actual’ skill and ‘perfect model’ skill in the recent period (1980–2001). Spread among the ensemble members are found to be large in simulations of India summer monsoon rainfall (ISMR) and Indian ocean dipole mode (IODM), indicating strong dependency of model simulated Indian summer monsoon on initial conditions. Multi-model ensemble performs better than the individual models in simulating ENSO indices, but does not perform better than the individual models in simulating ISMR and IODM. Decreased skill of multi-model ensemble over the region indicates amplification of errors due to existence of similar errors in the individual models. It appears that large biases in predicted SSTs over Indian Ocean region and the not so perfect ENSO-monsoon (IODM-monsoon) tele-connections are some of the possible reasons for such lower than expected skills in the recent period. The low skill of multi-model ensemble, large spread among the ensemble members of individual models and the not so perfect monsoon tele-connection with global SSTs points towards the importance of improving individual models for better simulation of the Indian monsoon.     

Simulation of Indian summer monsoon intraseasonal oscillations in a superparameterized coupled climate model: need to improve the embedded cloud resolving model

The characteristic features of Indian summer monsoon (ISM) and monsoon intraseasonal oscillations (MISO) are analyzed in the 25 year simulation by the superparameterized Community Climate System Model (SP-CCSM). The observations indicate the low frequency oscillation with a period of 30–60 day to have the highest power with a dominant northward propagation, while the faster mode of MISO with a period of 10–20 day shows a stationary pattern with no northward propagation. SP-CCSM simulates two dominant quasi-periodic oscillations with periods 15–30 day and 40–70 day indicating a systematic low frequency bias in simulating the observed modes. Further, contrary to the observation, the SP-CCSM 15–30 day mode has a significant northward propagation; while the 40–70 day mode does not show prominent northward propagation. The inability of the SP-CCSM to reproduce the observed modes correctly is shown to be linked with inability of the cloud resolving model (CRM) to reproduce the characteristic heating associated with the barotropic and baroclinic vertical structures of the high-frequency and the low-frequency modes. It appears that the superparameterization in the General Circulation Model (GCM) certainly improves seasonal mean model bias significantly. There is a need to improve the CRM through which the barotropic and baroclinic modes are simulated with proper space and time distribution.     

A Study of the Teleconnections in the Asian-Pacific Monsoon Region

The interactions among the Asian-Pacific monsoon subsystems have significant impacts on the climatic regimes in the monsoon region and even the whole world. Based on the domestic and foreign related research, an analysis is made of four different teleconnection modes found in the Asian-Pacific monsoon region, which reveal clearly the interactions among the Indian summer monsoon （ISM）, the East Asian summer monsoon （EASM）, and the western North Pacific summer monsoon （WNPSM）. The results show that： （1） In the period of the Asian monsoon onset, the date of ISM onset is two weeks earlier than the beginning of the Meiyu over the Yangtze River Basin, and a teleconnection mode is set up from the southwestern India via the Bay of Bengal （BOB） to the Yangtze River Basin and southern Japan, i.e., the ＂southern＂ teleconnection of the Asian summer monsoon. （2） In the Asian monsoon culmination period, the precipitation of the Yangtze River Basin is influenced significantly by the WNPSM through their teleconnection relationship, and is negatively related to the WNPSM rainfall, that is, when the WNPSM is weaker than normal, the precipitation of the Yangtze River Basin is more than normal. （3） In contrast to the rainfall over the Yangtze River Basin, the precipitation of northern China （from the 4th pentad of July to the 3rd pentad of August） is positively related to the WNPSM. When the WNPSM is stronger than normal, the position of the western Pacific subtropical high （WPSH） becomes farther northeast than normal, the anomalous northeastward water vapor transport along the southwestern flank of WPSH is converged over northern China, providing adequate moisture for more rainfalls than normal there. （4） The summer rainfall in northern China has also a positive correlation with the ISM. During the peak period of ISM, a teleconnection pattern is formed from Northwest India via the Tibetan Plateau to northern China, i.e., the ＂northern＂ teleconnection of the Asian summer monsoon. The     

The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century

The boreal summer Asian monsoon has been evaluated in 25 Coupled Model Intercomparison Project-5 (CMIP5) and 22 CMIP3 GCM simulations of the late twentieth Century. Diagnostics and skill metrics have been calculated to assess the time-mean, climatological annual cycle, interannual variability, and intraseasonal variability. Progress has been made in modeling these aspects of the monsoon, though there is no single model that best represents all of these aspects of the monsoon. The CMIP5 multi-model mean (MMM) is more skillful than the CMIP3 MMM for all diagnostics in terms of the skill of simulating pattern correlations with respect to observations. Additionally, for rainfall/convection the MMM outperforms the individual models for the time mean, the interannual variability of the East Asian monsoon, and intraseasonal variability. The pattern correlation of the time (pentad) of monsoon peak and withdrawal is better simulated than that of monsoon onset. The onset of the monsoon over India is typically too late in the models. The extension of the monsoon over eastern China, Korea, and Japan is underestimated, while it is overestimated over the subtropical western/central Pacific Ocean. The anti-correlation between anomalies of all-India rainfall and Niño3.4 sea surface temperature is overly strong in CMIP3 and typically too weak in CMIP5. For both the ENSO-monsoon teleconnection and the East Asian zonal wind-rainfall teleconnection, the MMM interannual rainfall anomalies are weak compared to observations. Though simulation of intraseasonal variability remains problematic, several models show improved skill at representing the northward propagation of convection and the development of the tilted band of convection that extends from India to the equatorial west Pacific. The MMM also well represents the space–time evolution of intraseasonal outgoing longwave radiation anomalies. Caution is necessary when using GPCP and CMAP rainfall to validate (1) the time-mean rainfall, as there are systematic differences over ocean and land between these two data sets, and (2) the timing of monsoon withdrawal over India, where the smooth southward progression seen in India Meteorological Department data is better realized in CMAP data compared to GPCP data.