Centre for Atmospheric and Oceanic Sciences (CAOS)

Indian Institute of Science (IISc), Bangalore

080- 22932505

office.caos@iisc.ac.in

Faculty Profile



Current Research


My work is focused on understanding Monsoons and its variability at various scales using models of climate. Being a computationally intensive topic, I have also worked on exploitation of High Performance Computing innovatively. I have also worked on emerging techniques such as AI/ML for climate and weather studies.



Details of Research Experience of Ravi S Nanjundiah


My research has been focussed on trying to understand Indian monsoon and its variability by investigations of a slew of atmospheric models including general circulation models (AGCMs), coupled AtmosphereOcean models (AOGCMs) as well as cloud system resolving models and on-line chemistry models , to assess the role of important factors and processes in the different observed facets of the monsoon. I have also assessed the performance of state of the art climate models in simulating/predicting the monsoon, its variability and its observed teleconnections so as to get an insight into how the skill in prediction could be improved . From the outset, I have also been deeply interested in the computational aspects and numerics of the models as well as use of machine learning methods. I have worked on improving the scalability of climate models, use of High Performance Computing, grid computing and computational accelerators for increasing computational throughput of climate models. All this has been achieved with successful collaboration with mathematicians, atmospheric scientists, oceanographers, hydrologists and computational scientists within and outside IISc, and with students.

A few of the investigations carried out are described briefly here to give a flavour of the problems addressed, the approach adopted and results obtained.

I started research on the Indian Summer Monsoon in 1986 with the development of a simple climate model which could simulate realistically some of the most important features of the observed intraseasonal variability of the Indian monsoon such as the northward propagations of cloud-bands/rain-belts from the equatorial Indian Ocean onto the Indian monsoon zone at iintervals of 2-6 weeks throughout the summer monsoon season. We found that in the model the propagations occur due to gradient in vertical moist static instability (Nanjundiah et al,1992). This could potentially be useful for forecasting such propagations. While working on this model, I also began to use alternative computational techniques such as parallel computing so as to enable faster simulations on the available computers. I have continued work on monsoon modelling as well as on computational aspects and numerics of models throughout my career.

I. Studies of the simulation of the mean monsoon


Role of the spectacular mountain ranges of the Indian subcontinent and Myanmar in the mean monsoon has been emphasized in one of the early theories of the monsoon in the beginning of the last century. A special feature of the Bay of Bengal which sustains the monsoon with cloud systems from the region moving onto the Indian landmass, is the low salinity water near the surface due to river runoff and rainfall. River runoff could decrease due to increased impounding of river-waters. It is thus important to understand the role of river runoff on the monsoon. It has been suggested that increasing aerosols, particularly, black carbon, may decrease the monsoon rainfall and the importance of considering the impact of aerosols, clouds and their interaction has been pointed out.

Prescription of cloud related processes are important for monsoons and its simulation. We examined cumulus closure in two AGCMs and found that simulation of tropical rainfall in general, and Indian summer monsoon in particular , were sensititve to the rate at which clouds reduce moist instability. Lower rates of instability reduction gave more realistic simulations (Jain et al 2013, Mishra et al 2008) .



a. Assessment of the performance of climate models in monsoon simulation and prediction


An analysis of the retrospective predictions by seven coupled ocean–atmosphere models from major forecasting centres of Europe and USA showed that they had reasonable skill in predicting the extremes of the Indian summer monsoon rainfall, particularly those associated with ENSO. However, despite large intermodel differences in the parameterizations, numerics etc., there was also a remarkable coherence between the failures of the models to predict the Equatorial Indian Ocean Oscillation (EQUINOO) and the monsoon in years in which EQUINOO played an important role. It was found that models were able to simulate ENSO–monsoon link realistically, whereas the link of monsoon rainfall with EQUINOO was simulated realistically by only one model – the ECMWF model. Furthermore, in most models the simulated link is opposite to the observed, suggesting that prediction of EQUINOO and its links with the monsoon need to be improved for improving monsoon predictions by these models (Nanjundiah et al, 2013). We found that while the CGCMs used in IPCC AR4 were also able to simulate the ENSO-Monsoon relationship realistically, almost all models failed to simulate the EQUINOO (Rajeevan and Nanjundiah 2009)-Indian monsoon relationship. Prediction of monsoons on seasonal scales analysed using DEMETER data showed similar behaviour (Nanjundiah et al 2013,

b. Impact of orography


Our investigation of what happens to the monsoon when the mountains over different parts of the globe are removed in an AGCM (the NCMRWF GCM) showed that, with the exception of African orography, removal of orography in any other part of the world including proximate and remote regions such as the Rockies and the Andes reduced the monsoon rainfall and delayed its onset, these effects being largest for Western Himalayas (Chakraborty et al 2002, 2006). The absence of African orography strengthened the low level westerly jet and the rainfall over the Indian monsoon region because of non-linear feedbacks between rainfall and circulation that had been ignored in previous studies.

c. Monsoon & Ocean-Land-Atmosphere interaction


Comparison of a 100 year run of the Community Climate System Model (CCSM3) in which the river runoff is included with another similar run in which there is no river runoff showed that when the river discharge was shut off, global average sea surface temperature (SST) rose by about 0.5°C and the monsoon rainfall increased by about 10% with a large increase in the eastern Bay of Bengal and along the west coast of India. In addition, the frequency of occurrence of La Ni˜na-like cooling events in the equatorial Pacific increased and the correlation between ISMR and Pacific SST anomalies became stronger. The teleconnection between the SST anomalies in the Pacific and monsoon was effected via upper tropospheric meridional temperature gradient and a shift of the North African Asian Jet axis (Vinayachandran et al, 2015). Further studies with modulating runoff from the Amazon showed that Studies about the role of Amazonian runoff on climate showed that the Atlantic Meridional Overturning Circulation (AMOC) strengthened in its absence. Lack of Amazonian runoff also triggered a bipolar seesaw in SST across the thermal equator with warming to the north and cooling to the south. The boreal summer rainfall in the tropical Atlantic Ocean sector responded to this change in SST (Jahfer et al, 2020)

d. Intraseaonal Variations, Trends and Structure


We have stuided the trends in intraseaonal variations at decadal and longer scales (Karmakar et al, 2017). We found that while the long term trend of mean rainfall showed a decreasing trend, we also found a reduction in strength of low-frequency (20-60 days), while there was an increase in variability at higher frequencies (less than 10 days). We also find that there was an increase in sporadic rainfall events especially during the break phase and a reduction in of such events during the active phase. These were understood using an AGCM with prescibed heating over the Central Indian Region. The rich structure of intraseaonal Variations was studied using rainfall data from TRMM and other sources. Two dominant modes of variability with periodicities of 10–20 and 20–60 days were found, with the latter being strongly modulated by sea surface temperature. The 20–60 day mode showed northward propagation from the equatorial Indian Ocean linked with eastward-propagating modes of convective systems over the tropics. The 10–20 day mode showed a complex space–time structure with a northwestward-propagating anomalous pattern emanating from the Indonesian coast. This pattern was found to be interacting with a structure that emerged from higher latitudes which propagated into the Indian region (Karmakar et al 2017)

We have developed a technique for analysis of space-time variation of tropical precipitation using wavelets (Shanker and Nanjundiah, 2004, Chakraborty and Nanjundiah, 2012). This technique was used to quantify the spatial extent (scale) and centre of these propagating convective bands, as well as the time period associated with the different spatial scales. Results suggested that during a good monsoon year the spatial scale of this oscillation was about 30o centered around 10 oN. During weak monsoon years, the scale of propagation decreased and the centre shifted farther south, closer to the equator. A strong linear relationship was obtained between the center/scale of convective wave bands and intensity of monsoon precipitation over the Indian land on the interannual time scale. Moreover, the spatial scale and its centre during the break monsoon period were found to be similar to an overall weak monsoon year. Based on this analysis, a new index was proposed to quantify the spatial scales associated with propagating convective bands. This automated wavelet-based technique could be used to study meridional propagationof convection in a large volume of datasets from observations and model simulations.

e.Study of Midtropospheric Cyclones


We studied Midtroposhperic Cyclones (MTC) all across the globe (Kushwaha et al, 2021). These are moist synoptic systems with distinct midtropospheric vorticity maxima and weak signatures in the lower troposphere. Composites and statistics of tropical MTCs were constructed and compared with monsoon lows and depressions [together, lower-troposphere cyclones (LTCs)]. Tracking MTC over South Asia, we found that MTCs changed character during their life, i.e., their track comprised of MTC and LTC phases. The highest MTC-phase density and least motion were noticed over the Arabian Sea, followed by the Bay of Bengal and the South China Sea. An MTC-phase composite showed an east–west-tilted warm-core above a deep cold-core temperature anomaly with maximum vorticity at 600 hPa. In contrast, the LTC-phase showed a shallow cold core below 800 hPa and a warm upright temperature anomaly with a lower-tropospheric vorticity maximum. Globally, systems with MTC-like morphology were observed over west and central Africa and the east and west Pacific in boreal summer. In boreal winter, regions that support MTCs include northern Australia, the southern Indian Ocean, and southern Africa. MTC fraction is higher equatorward where there is a cross-equatorial low-level jet that advected oppositely signed vorticity, whereas LTCs were more prevalent farther poleward. We found that relationship between differential vorticity (the difference between middle and lower levels) and the height of peak vorticity for cyclonic centers was bimodal. One peak, around 600 hPa, corresponded to MTCs, while the second, at approximately 900 hPa,were from LTCs.

f. Convecion at Shorter Scales


We have studied convection over Bay of Bengal using a cloud system resolving model (Jain et al, 2018). Equatorward propagating precipitation episodes over the Bay of Bengal were noticed ( as in previous previous observational studies). The mechanism governing this southward propagation was determined. The mechanisms included mean surface to midtropospheric wind shear driving the convection orthogonal to the lower tropospheric winds and the gravity currents generated by outflow from convection initiated by the diurnally varying land-ocean circulations dispersing southwards.



II . Monsoons, Aerosols & Clouds


Aerosols and their interaction with atmosphere especially in modulating radiative heating can have a significant impact on monsoons. Over India black carbon aerosols (from vehicular emissions) and dust can play a significant role in modulating monsoons.

When we incorporated the atmospheric heating effect of black carbon aerosols (and cooling effect on the ground), we found that the strength of the mean monsoon increased. However, the regions where significant changes in precipitation occurred were sensitive to the cumulus parameterization used. (Chakraborty et al, 2004, Chakraborty et al 2012). Our studies have also shown that aerosol radiative forcing over geographically remote regions such as Eastern China could impact the Indian Monsoon (Chakraborty et al 2014). We have also assessed the simulation of aerosol concentrations over India in an on-line chemistry model (Govardhan et al 2015, 2016). We further assessed the regional aerosol radiative effects under the SWAMI campaign (Pathak et al 2019, 2020) and also developed a comprehensive dataset of aersol concentration by merging Satellite and terrestrial data using data assimilation techniques.

Studies with On-line Chemistry Models


While most studies focus on the near-surface abundance and impacts of BC, we examined the implications of sharp and confined layers of high BC concentration (called elevated BC layers) at altitudes of more than 4 km over the Indian region using an online regional chemistry transport model (WRF-Chem). Our study demonstrated that high-flying aircraft are the most likely cause of these elevated BC layers. Furthermore, we showed that such aircraft-emitted BC can be transported to upper tropospheric or lower stratospheric heights ( ∼ 17km) aided by the strong monsoonal convection occurring over the region, which is known to overshoot the tropical tropopause, leading to the injection of tropospheric air mass (along with its constituent aerosols) into the stratosphere. We showed observational evidence for such an intrusion of tropospheric BC into the stratosphere over the Indian region using extinction coefficient and particle depolarisation ratio data from CALIOP Lidar on-board the CALIPSO satellite (Goverdhan et al,2017)

Air Quality Prediction


Modelling and prediction of Air Quality is gaining importance in India. I have been associated with the development of an Air Quality Early Warning Sytem for Delhi and its environs. The highlight of this system is that Chemicalinformation from satellite and terrestrial ground stations have been incorporated using a Data Assimilation System (Kumar et al 2020, Jena et al, 2020). Based on this a Decision support is being developed for authorities to take informed decisions durin extreme air quality events.


III Machine Learning Techniques for Climate Change & Seasonal Prediction:


We have studied the impact of possible anthropogenic climate change on smaller scales (such as that of a river basin or a meteorological subdivision) than those that can be resolved by IPCC scenario models, by using machine learning downscaling techniques (Tripathi et al, 2006, Anandhi et al, 2012). We have shown that forecasts of the Indian summer monsoon rainfall on the seasonal scale using machine learning techniques, such as stacked encoder have skills comparable to the operational statistical models (Saha et al, 2016,2020). Models were also developed for the different homogeneous regions of India (Saha et al, 2017a). We also developed models for early part of the (June and July) and later part of the Monsoon, using deep neural networks such as stacked autoencoder and the ensmeble network tree regression tree technique (Saha et al, 2017b). Also based upon upon Deep Learning techniques, we developed a prediction model for EQUINOO and ENSO. The model for EQUINOO is the first of its kind (Saha and Nanjundiah 2020). Models based on LSTM were developed to study and predict active and break spells of the monsoon (Vishwanath et al, 2019) With enhanced concern about increased localised extreme precipitation events, we have developed techniques using as ConvLSTM (Kumar et al, 2022) and Unet based system on cubed sphere (Singh et al, 2023).


IV Numerical Techniques for AGCMs


We have investigated the impact of numerics and developed a framework for deriving splitting methods for semi-linear ordinary differential equations and partial differential equations (Murthy and Nanjundiah, 2000). We developed a technique for grid refinement for the spectral technique using reparameterisation maps tailored for studying tropical convection and teleconnections. Grid refinement in spectral models is a much more challenging task than in grid-point models and only a handful of such efforts have been attempted (Janakiraman et al, 2012). The technique so developed was used in a bartropic model.


V HPC & Climate Modelling:


Climate models are computationally intensive and using conventional computers have always been expensive. I implemented a simple climate on India’s first parallel computer viz Flosolver (at NAL). This implementation in 80s was the first such implementation in the country and perhaps one of the earliest such implementations in the world.

Scalability and throughput are important issues while using High Performance Computing. Any improvements in these implies a faster and more efficient computer at no additional cost. My research (in collaboration with computational scientists) has shown that these can be improved through load-balancing, message compression and use of computational accelerators. Our work on load balancing (Sundari et al, 2009) for a climate-system model showed that moving high computational load related to radiation to less-loaded processors computing ocean and other components, led to about 15% improvement in overall throughput. This perhaps was the first of its kind for a multi-component climate system model.

The trend in computational architecture is to move towards accelerators such as GPUs (for lowering of energy requirement) and increasing throughput at minimal cost. I have worked on exploiting GPUs for climate modelling (Korwar et al, 2014). Radiation computations can be a significant part of a climate model’s execution time. By offloading the most time-consuming routines of radiation onto GPU’s and exploiting asynchronous computing due to the fact that radiation is a slowly varying function of time, we could completely offset the time consumed in this part of execution. In the process these parts of the codes could be called more frequently and hence could result in higher accuracy. The asynchronous mode of execution was replicated on Xeon-Phi multicore sytems (Kashyap et al,2017)

Interprocessor communication is one of the biggest bottlenecks in parallel computing. Bigger the size of the communicated message, higher could be the overhead. My work (Kumar et al, 2008) on messagecompression exploited the fact that the model state does not change significantly between two consecutive timesteps and this could be exploited to reduce inter-processor communication. By sending information only about the changed parts of the message the speedup increases by about 18%. This was the first application of message-compression to a climate model.

Grid Computing for Climate Modelling


Grid computing helps to agglomerate resources at geographically distinct locations. In a resources-starved country like India, it is a good alternative to installing a large computer at a single location. We built a computational grid consisting of clusters at CAOS and SERC that could be used for climate-system model simulations (Sundari et al 2012, 2011) . This grid exploited the feature that a climate-system model consists of loosely coupled sub-components running concurrently with inter-component communication being low (in contrast to intra-component communication which could be much higher). The middleware developed for this purpose could reconfigure computations whenever new/existing systems became available/unavailable. The ideas thus developed can also be used on future exa-scale computers .



Highlights of My Recent Work as Director IITM


During most of this period, I was Director of the Indian Institute of Tropical Meteorology(IITM), Pune. IITM is an institute devoted to the study of Tropical Meteorology addressing a wide range of problems in weather, climate variability and change and for developing forecasting tools to be used by Indian Meteorological Department and other agencies. It has about 150 tenured scientists, about 150 project scientists and about 90 students.The areas of work include development of models at various scales, field observations to study clouds and their interactions with aerosols and studying climate change using models and observations. As Director of IITM I was also the Mission Director of Monsoon Mission - a mission mode project to improve forecasts at all scales and their applications to various sectors. Some of the highlights during this period are: