Calculation of Si NMR shifts of silicate complexes with carbohydrates, amino acids, and muhicarboxylic acids: potential role in biological silica utilization

The existence of ether or ester-like complexes of silicate with organic compounds has long been debated in the literature on biological utilization of silicon. Comparison of theoretically calculated ²⁹Si NMR chemical shifts for such complexes with experimentally measured values in biological systems could provide a diagnostic tool for identifying which, if any of these molecules exist under physiological conditions. Results are presented here for ab initio molecular orbital calculations of ²⁹Si NMR shifts and formation energies of silicate complexes with polyalcohols, sugar-acids, pyranose sugars, amino acids and multicarboxylic acids. The effects of functional group and molecular structure including ligand size, denticity, ring size, silicon polymerization and coordination number on calculated ²⁹Si shifts were considered. The potential role of such compounds in biological silica utilization pathways is discussed.²⁹Si NMR shifts and energies were calculated at the HF/6-311+G(2d,p)//HF/6-31G* level. The main result is that only five-membered rings containing penta- and hexa-coordinated Si can explain experimentally observed resonances at ∼ −101 and −141 ppm. Further, the heptet observed in ¹H-²⁹Si coupled spectra can only be explained by structures where Si bonds to oxygens atoms in H-C-O-Si linkages with six symmetrically equivalent H atoms.While compounds containing quadra-coordinated silicon may exist in intracellular silicon storage pools within diatoms, calculated reaction energies suggest that the organism has no thermodynamic advantage in taking up extracellular organ-silicate compounds, instead of silicic acid, from the ambient aqueous environment. Hyper-coordinated complexes are deemed unlikely for transport and storage, though they may exist as transient reactive intermediates or activated complexes during enzymatically- catalyzed silica polymerization, as known previously from sol-gel silica synthesis studies.     

The impact of northern Indian Ocean rivers on the Bay of Bengal using NEMO global ocean model

The effect of river runoff over the northern Indian Ocean(NIO) especially over the Bay of Bengal(Bo B) has been studied using global Nucleus for European Modelling of the Ocean(NEMO). Two sensitivity experiments, with and without river runoff are conducted and the influence of river runoff on the Indian Ocean hydrography,stratification and circulation features are studied. It is found that due to river runoff surface salinity over the northern Bo B decreases by more than 5 and the East India Coastal Current strengthens by 2 cm/s during post monsoon season. The fresh river water reaches up to 15°N in the Bo B and is the main cause for low salinity there.Sea surface temperature in the northwestern Bo B increases by more than 0.2℃ due to the river runoff in summer monsoon while surface cooling upto 0.2℃ is seen in north-west part of Bo B in winter season. The seasonal mixed layer depth in the region is found to be dependent on river runoff. The effect of vertical shear and Brunt Vaisala frequency on stratification is also examined. The ocean water becomes highly stratified up to 3 035 m due to the river runoff. It is found that the energy required for mixing is high in the northern and coastal Bo B.     

Upper ocean high resolution regional modeling of the Arabian Sea and Bay of Bengal

In this paper, effort is made to demonstrate the quality of high-resolution regional ocean circulation model in realistically simulating the circulation and variability properties of the northern Indian Ocean(10°S–25°N,45°–100°E) covering the Arabian Sea(AS) and Bay of Bengal(BoB). The model run using the open boundary conditions is carried out at 10 km horizontal resolution and highest vertical resolution of 2 m in the upper ocean.The surface and sub-surface structure of hydrographic variables(temperature and salinity) and currents is compared against the observations during 1998–2014(17 years). In particular, the seasonal variability of the sea surface temperature, sea surface salinity, and surface currents over the model domain is studied. The highresolution model's ability in correct estimation of the spatio-temporal mixed layer depth(MLD) variability of the AS and BoB is also shown. The lowest MLD values are observed during spring(March-April-May) and highest during winter(December-January-February) seasons. The maximum MLD in the AS(BoB) during December to February reaches 150 m (67 m). On the other hand, the minimum MLD in these regions during March-April-May becomes as low as 11–12 m. The influence of wind stress, net heat flux and freshwater flux on the seasonal variability of the MLD is discussed. The physical processes controlling the seasonal cycle of sea surface temperature are investigated by carrying out mixed layer heat budget analysis. It is found that air-sea fluxes play a dominant role in the seasonal evolution of sea surface temperature of the northern Indian Ocean and the contribution of horizontal advection, vertical entrainment and diffusion processes is small. The upper ocean zonal and meridional volume transport across different sections in the AS and BoB is also computed. The seasonal variability of the transports is studied in the context of monsoonal currents.     

Upper Ocean Four-Dimensional Variational Data Assimilation in the Arabian Sea and Bay of Bengal

A regional ocean circulation model with four-dimensional variational data assimilation scheme is configured to study the ocean state of the Indian Ocean region (65°E–95°E; 5°N–20°N) covering the Arabian Sea (AS) and Bay of Bengal (BoB). The state estimation setup uses 10 km horizontal resolution and 5 m vertical resolution in the upper ocean. The in-situ temperature and salinity, satellite-derived observations of sea surface height, and blended (in-situ and satellite-derived) observations of sea surface temperature alongwith their associated uncertainties are used for data assimilation with the regionally configured ocean model. The ocean state estimation is carried out for 61 days (1 June to 31 July 2013). The assimilated fields are closer to observations compared to other global state estimates. The mixed layer depth (MLD) of the region shows deepening during the period of assimilation with AS showing higher MLD compared to the BoB. An empirical forecast equation is derived for the prediction of MLD using the air–sea forcing variables as predictors. The surface and sub-surface (50 m) heat and salt budget tendencies of the region are also investigated. It is found that at the sub-surface, only the advection and diffusion temperature and salt tendencies are important.     

Low-latitude total ozone measurements in the Brazilian sector

Regular measurements of the atmospheric ozone in the Brazilian sector were started at Cachoeira Paulista (22.7°S, 45.0°W), and Natal (5.8°S, 35.2°W) in May 1974 and November 1978, respectively. The results of the total ozone measurements carried out at these two stations up to 1981 are presented in this communication and compared with other low-and mid-latitude stations. Although Natal is an equatorial station, it presents a prominent annual variation, and the average total ozone content is high compared to satellite measurements. During 1977–78, abnormally low values of total ozone were observed at Cachoeira Paulista. Some preliminary results about the QBO 9quasi-biennial oscillation) during 1974–81 are also presented.     

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.