Implementing a nonlinear gravity wave (GW) parameterization into a mechanistic middle and upper atmosphere model, which extends to the lower thermosphere (160 km), we study the response of the atmosphere in terms of the circulation patterns, temperature distribution, and migrating terdiurnal solar tide activity to the upward propagating small scale internal GWs originating in the lower atmosphere. We perform three test simulations for the Northern Hemisphere winter conditions in order to assess the effects of variations in the initial GW spectrum on the climatology and tidal patterns of the mesosphere and lower thermosphere. We find that the overall strength of the source level momentum flux has a relatively small impact on the zonal mean climatology. The tails of the GW source level spectrum, however, are crucial for the lower thermosphere climatology. With respect to the terdiurnal tide, we find a strong dependence of tidal amplitude on the induced GW drag, generally being larger when GW drag is increased.

In first part of the study we discuss impact of dynamical core in simulation of tropical rainfall. Over years many new dynamical cores have been developed for atmospheric models to increase efficiency and reduce numerical errors. CAM3.1 gives an opportunity to study the impact of the dynamical core on simulations with its three dynamical cores namely Eulerian spectral(EUL) , Semilagrangian dynamics(SLD) and Finite volume(FV) coupled to a single parametrization package. A past study has compared dynamical cores of CAM3 in terms on tracer transport and has showed advantages using FV in terms of tracer transport. In this study we compare the dynamical cores in climate simulations and at their optimal configuration, which is the intended use of the model. The model is forced with AMIP type SST and rainfall over seasonal, interannual scales is compared. The significant differences in simulation of seasonal mean exist over tropics and over monsoon regions with observations and among dynamical cores. The differences among EUL and SLD, which use spectral transform methods are lesser compared that of with FV clearly indicating role of numerics in differences. There exist major errors in simulation of seasonal cycle in all dynamical cores and errors in simulation of seasonal means over many regions are associated with errors in simulation of seasonal cycle such as over south china sea. Seasonal cycle in FV is weaker compared to SLD and EUL. The dynamical cores exhibit different interannual variability of rainfall over Indian monsoon region, the period of maximum power corresponding to a dynamical core differs substantially with another. From this study there seems no superiority associated with FV dynamical core over all climate scales as seen in tracer transport. The next part of the study deals with impact of diurnal ocean atmosphere coupling in an AGCM,CAM3.1. Due to relatively low magnitude of diurnal cycle of SST and lack of SST observations over diurnal scales current atmospheric models are forced with SSTs of periods grater than a day. CAM 3.1 standalone model is forced with monthly SSTs but the interpolation is linear to every time step between any two months and this linear interpolation implies a linear diurnal and intraseasonal variation of SST which is not true in nature. To test the sensitivity of CAM3.1 to coupling of SST on diurnal scales, we prescribed over tropics(20S20N) a diurnal cycle of SST over daily mean interpolated SST of different magnitudes and phase comparable to observations. This idea of using a diurnal cycle of SST retaining seasonal mean SST in an atmospheric model is novel and provides an interesting frame work to test sensitivity of model to interpolations used in coupling of boundary conditions. Our analysis shows a high impact of using diurnal cycle of SST on simulation of mean rainfall over tropics. The impact in a case where diurnal cycle of SST is fixed and retained to daily mean SST implies that changes associated with a coupled model are to some extent due to change in representation of diurnal cycle of SST. A decrease of excess rainfall over western coast of Bay of Bengal and an increase of rainfall over northern bay of Bengal in such case is similar to the improvement due to coupling atmospheric model to a slab ocean model. This also implies that problems with current AMIP models in simulation of seasonal mean Indian monsoon rainfall could be due to erroneous representation of diurnal cycle of SST in models over this region where the diurnal cycle of SST is high in observations. The high spatial variability of the impact in various cases over tropics implies that a similar spatial variation of diurnal cycle could be important for accurate simulation of rainfall over tropics. Preliminary analysis shows that impact on rainfall was due to changes in moisture convergence. We also hypothesized that diurnal cycle of SST could trigger convection over regions such as northern Bay of Bengal and rainfall convergence feedback sustains it. The impact was also found on simulation of internal interannual variability of rainfall

Indian summer monsoon (ISM) is an important component of the tropical climate system, known for its regular seasonality and abundance of rainfall over the country. The droughts and floods associated with the year-to-year variation of the average seasonal rainfall have devastating effect on people, agriculture and economy of this region. The demand for prediction of seasonal monsoon rainfall, therefore, is overwhelming. A number of attempts to predict the seasonal mean monsoon have been made over a century, but neither dynamical nor empirical models provide skillful forecasts of the extremes of the monsoon such as the unprecedented drought of 2002. This study investigates the problems and prospects of extended range monsoon prediction. An evaluation of the potential predictability of the ISM with the aid of an ensemble of Atmospheric General Circulation Model (AGCM) simulations indicates that the interannual variability (IAV) of ISM is contributed equally by the slow boundary forcing (‘externally’ forced variability) and the inherent climate noise (‘internal’ variability) in the atmosphere. Success in predicting the ISM would depend on our ability to extract the predictable signal from a background of noise of comparable amplitude. This would be possible only if the ‘external’ variability is separable from the ‘internal’ variability. A serious effort has been made to understand and isolate the sea surface temperature (SST) forced component of ISM variability that is not strongly influenced by the ‘internal’ variability. In addition, we have investigated to unravel the mechanism of generation of ‘internal’ IAV so that the method of isolating it from forced variability may be found. Since the primary forcing mechanism of the monsoon is the large-scale meridional gradient of deep tropospheric heat sources, large-scale changes in tropospheric temperature (TT) due to the boundary forcing can induce interannual variations of the timing and duration of the monsoon season. The concept of interannually varying monsoon season is introduced here, with the onset and withdrawal of monsoon definitions based on the reversal of meridional gradient of TT between north and south. This large scale definition of the monsoon season is representative of the planetary scale influence of the El Ni˜no Southern Oscillation (ENSO) on monsoon through the modification of TT and the cross equatorial pressure gradient over the ISM region. A sig- nificant relationship between ENSO and monsoon, that has remained steady over the decades, is discovered by which an El Ni˜no (La Ni˜na) delays (advances) the onset, advances (delays) the withdrawal and suppresses (enhances) the strength of the monsoon. The integral effect of the meridional gradient of TT from the onset to withdrawal proves to be a useful index of seasonal monsoon which isolates the boundary forced signal from the influence of internal variations that has remained steady even in the recent decades. However, consistent with the estimates of potential predictability, the boundary forced variability isolated with the above definitions explains only about 50% of the total interannual variability of ISM. Detailed diagnostics of the onset and withdrawal processes are performed to understand how the ENSO forcing modifies the onset and withdrawal, and thus the seasonal mean monsoon. It is found that during an El Ni˜no, the onset is delayed due to the enhanced adiabatic subsidence that inhibits vertical mixing of sensible heating from the warm landmass during pre-monsoon months, and the withdrawal is advanced due to the horizontal advective cooling. This link between ENSO and monsoon is realized through the advective processes associated with the stationary waves in the upper troposphere set up by the tropical ENSO heating. The remaining 50% of the monsoon IAV is governed by internal processes. To unravel the mechanism of the generation of internal IAV, we perform another set of AGCM simulations, forced with climatological monthly mean SSTs, to extract the pure internal IAV. We find that the spatial structure of the intraseasonal oscillations (ISOs) in these simulations has significant projection on the spatial structure of the seasonal mean and a common spatial mode governs both intraseasonal and interannual variability. Statistical average of ISO anomalies over the season (seasonal ISO bias) strengthens or weakens the seasonal mean. It is shown that interannual anomalies of seasonal mean are closely related to the seasonal mean of intraseasonal anomalies and explain about 50% of the IAV of the seasonal mean. The seasonal mean ISO bias arises partly due to the broadband nature of the ISO spectrum, allowing the intraseasonal time series to be aperiodic over the season and partly due to a non-linear process where the amplitude of ISO activity is proportional to the seasonal bias of ISO anomalies. The later relationship is a manifestation of the binomial character of the rainfall time series. The remaining part of IAV may arise due to the complex land-surface processes, scale interactions, etc. We also find that the ISOs over the ISM region are not significantly modulated by the Pacific and Indian Ocean SST variations. Thus, even with a perfect prediction of SST, only about 50% of the observed IAV of ISM could be predicted with the best model in forced mode. Even so, prediction of all India rainfall (AIR) representing the average conditions of the whole country and the season may not always serve the purposes of monsoon forecasting. One reason is the large inhomogeneities in the rainfall distribution during a normal seasonal monsoon. Agriculture and hydrological sector could benefit more if provided with regional scale forecasts of active/break spells 2-3 weeks ahead. Therefore, we advocate an alternative strategy to the seasonal prediction. Here, we present a method to estimate the potential predictability of active and break conditions from daily rainfall and circulation from observations for the recent 24 years. We discover that transitions from break to active conditions are much more chaotic than those from active to break, a fundamental property of the monsoon ISOs. The potential predictability limit of monsoon breaks (∼20 days) is significantly higher than that of the active conditions (∼10 days). An empirical real- time forecasting strategy to predict the sub-seasonal variations of monsoon up to 4 pentads (20 days) in advance is developed. The method is physically based, with the consideration that the large-scale spatial patterns and slow evolution of monsoon intraseasonal variations possess some similarity in their evolutions from one event to the other. This analog method is applied on NOAA outgoing longwave radiation (OLR) pentad mean data which is available on a near real time basis. The elimination of high frequency variability and the use of spatial and temporal analogs produces high and useful skill of predictions over the central and northern Indian region for a lead-time of 4-5 pentads. An important feature of this method is that, unlike other empirical methods to forecast monsoon ISOs, this uses minimal time filtering to avoid any possible end-point effects, and hence it has immense potential for real-time applications.

Interannual variation of Indian summer (June-September: JJAS) monsoon rainfall (ISMR) depends on its relative intensity during early (June-July: JJ; contribution 52%) and late (August-September: AS; contribution 49%) phases. Apart from variations in sea surface temperature (SST), the primary reasons behind the variability during JJ and AS can be very different due to change in climatic conditions on account of post-onset processes. Here, using a high resolution general circulation model with seasonally varying climatological SST, mechanisms those govern the intensity of rainfall during JJ and AS are investigated. There is no significant relation-ship between intensity of precipitation over Indian region in JJ and AS. Moreover, the factors determining early monsoon (JJ) precipitation are different than that for late monsoon (AS). In absence of interannual SST variation, pre-monsoon soil moisture do not play a significant role for the interannual variation of monsoon precipitation over India. A large scale oscillation of the ITCZ is noticed on interannual time scale spanning from around 60◦E to 150◦E that brings spatially coherent flood and drought over this region. Early monsoon precipitation has a larger dependency on spring snow depth over Eurasia and phase of the upper tropospheric Rossby wave in May. However, late monsoon precipitation over India is mainly governed by the intensity and time scale of the intraseasonally varying convective cloud bands. This study suggests that early monsoon (JJ) precipitation over Indian region is more correlated with pre-monsoon signatures of land-atmosphere parameters. However, in later parts after the onset (AS), the monsoon intensity is primarily driven by its internal dynamics and characteristics of intraseasonal oscillation.

Abstract The diurnal cycle is a dominant mode of sea surface temperature (SST) variability in trop-ical oceans, that influences air-sea interaction and climate processes. Diurnal variability of SST generally ranges from ~0.1 to 2.0◦C and is controlled by atmospheric fluxes of heat and momentum. In the present study, the response of intraseasonal variability (ISV) of SST in the Bay of Bengal (BoB) to diurnal atmospheric forcings, during the summer monsoon of 2007, has been examined using an Ocean General Circulation Model (OGCM). The model is based on the Modular Ocean Model Version 4 (MOM4p0), having a horizontal resolution of 0.25◦ and 40 vertical levels, with a fine resolution of 5 m in the upper 60 m. Numerical experiments were conducted by forcing the model with daily and hourly atmospheric forcings to examine the SST-ISV modulation with the diurnal cycle. Additional experiments were performed to determine the relative role of diurnal cycle in solar radiation and winds on SST and mixed layer depth (MLD). Since salinity, which is decisive in SST variability, varies meridionally in the BoB, two locations were selected for analyses: one in the northern bay at 89◦E, 19◦N where salinity is lower and the other in the southern bay at 90◦E, 8◦N where salinity is higher, as well as observations are available from Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) buoy for comparision with model simulation. Diurnal atmospheric forcings modify SST-ISV in both southern and northern bay. SST-ISV in the southern bay, is dominantly controlled by the diurnal cycle of insolation, while in the northern bay, diurnal cycle of insolation and winds have comparable contribution. Diurnal cycle enhanced the amplitude of 3 selected intraseasonal events in the southern bay and 3 out of the 6 events in the northern bay, during the study period. In the southern bay, simulated SST variability with hourly forcing was closer to the observations from RAMA, implying that incorporating the diurnal cycle in model forcing rectifies SST-ISV. Moreover, SST obtained with diurnal forcing consists of additional fluctuations at higher frequencies within and in between intraseasonal events; such fluctuations are absent with daily forcing. The diurnal variability of SST is significant during the warming phase of intraseasonal events and reduces during the cooling phase. Diurnal amplitude of SST decreases with depth; depth dependence also being larger during the warming phase. SST-ISV modulation with diurnal forcing results from the diurnal cycle of upper ocean heat fluxes and vertical mixing. Diurnal warming and cooling result in a net gain or loss of heat in the mixed layer after a day’s cycle. When the retention (loss) of heat in the mixed layer increases with diurnal forcing during the warming (cooling) phase of intraseasonal events, the daily mean SST rise (fall) becomes higher, amplifying the intraseasonal warming (cooling). In the southern bay, SST-ISV amplification is mainly controlled by the diurnal variability of MLD, which modifies the heat fluxes. Increased intraseasonal warming with diurnal forcing results from the increase in radiative heating, due to the shoaling of the daytime mixed layer. Amplified intraseasonal cooling is dominantly con-trolled by the strengthening of sub-surface processes, due to the nocturnal deepening of mixed layer and increased temperature gradients below the mixed layer. In the northern bay, SST-ISV modulation with diurnal forcing is not as large as that in the southern bay. The mean increase in SST-ISV amplitudes with diurnal forcing is ~0.16◦C in the southern bay, while it is only ~0.03◦C in the northern bay. Reduced response of SST-ISV to diurnal forcings in the northern bay is related to the weaker diurnal variability of MLD. Salinity stratification limits diurnal variability of mixed layer in the northern bay, unlike in the southern bay. The seasonal (June - September) mean diurnal amplitude of MLD is ~15 m in the southern bay, while it is reduced to ~1.5 m in the northern bay. Diurnal variability of MLD, spanning only a few meters is not sufficient to create large modifications in mixed layer heat fluxes and SST-ISV in the northern bay. The vertical resolution of the model limits the shallowing of mixed layer to 7.5 m, thus restricting the diurnal variability of simulated MLD.