The vast and complex oceans that are optically opaque are acoustically transparent, enabling characterization of physical and biological bodies and processes of sea using sound as a premier tool. Lack of direct observations of vertical profiles of velocimeters and/or temperature and salinity, from which sound speed can be calculated, limits specifications and investigation of temporal and spatial variabilities of the three-dimensional structure of the sound speed in the oceans. In this study, the authors demonstrate estimation of sound speed profiles (SSPs) from surface observations using an artificial neural network (ANN) method. Surface observations from a mooring in the central Arabian Sea are used as a proxy to the satellite observations. The ANN-estimated SSPs had a root-mean-square error of 1.16 m/s and a coefficient of determination of 0.98. About 76% (93%) of the estimates lie within plusmn1 m/s (plusmn2 m/s) of the SSPs obtained from in situ temperature and salinity profiles

This study examines the benefit of using Ocean Mean Temperature (OMT) to aid in the prediction of the sign of Indian Summer Monsoon Rainfall (ISMR) anomalies. This is a statistical examination, rather than a process study. The thermal energy needed for maintaining and intensifying hurricanes and monsoons comes from the upper ocean, not just from the thin layer represented by sea surface temperature (SST) alone. Here, we show that the southwestern Indian OMT down to the depth of the 26 °C isotherm during January–March is a better qualitative predictor of the ISMR than SST. The success rate in predicting above- or below-average ISMR is 80% for OMT compared to 60% for SST. Other January–March mean climate indices (e.g., NINO3.4, Indian Ocean Dipole Mode Index, El Niño Southern Oscillation Modoki Index) have less predictability (52%, 48%, and 56%, respectively) than OMT percentage deviation (PD) (80%). Thus, OMT PD in the southwestern Indian Ocean provides a better qualitative prediction of ISMR by the end of March and indicates whether the ISMR will be above or below the climatological mean value.

Studies of Indian summer monsoon rainfall (ISMR: June–September) on regional scales are critically important for various applications related to agriculture and water management in India. Based on the coherent rainfall over regional scales, the India Meteorological Department defined four so‐called homogeneous regions: northwest India (NWI), northeast India (NEI), central India (CI), and south peninsula India (SPIN). Here we present the salient features of daily rainfall behavior over these four regions and their association with the strength of ISMRs between strong and weak monsoons. Our results reveal that rainfall rates are primarily stronger (weaker) over NWI, CI, and SPIN during strong (weak) monsoons, while rainfall rates over NEI show no remarkable changes between strong and weak monsoons. Specifically, stronger rainfalls over NWI, CI, and SPIN during strong monsoons are generally caused by prolonged and strong wet spells followed by short and weak dry spells, primarily during the mature phase and the withdrawal phase. In contrast, weaker rainfalls over NWI, CI, and SPIN during weak monsoons are mainly caused by the prolonged and strong dry spells, which mostly occur in July–August, followed by short wet spells. The distinctive rainfall features over each region are closely associated with the characteristics of regional convective activity over each region. The rainfall rates over NEI appear insensitive to the strength of ISMRs. Finally, a probability density function analysis indicates that the rainfall rates over the four homogeneous regions between strong and weak monsoons can be characterized by the likelihood of occurrence of different rainfall ranges.

We analyzed the influence of environmental parameters on the temporal variation of atmospheric carbon dioxide (CO ₂ ) mixing ratios in two environmentally contrasting Indian sites, Dehradun (30.1°N, 77.4°E, humid subtropical station) and Gadanki (13.5°N, 79.18°E, dry tropical station), from October 2010 to September 2011. The annual range of mixing ratios is low in Gadanki as compared to those of Dehradun because of relatively less monthly variation in temperature and relative humidity (RH) at Gadanki. At both the stations, the minimum mixing ratios are present during the high ecosystem productivity seasons in the afternoon hours. The maximum values are in the early morning hours. However, low wind speed conditions control the unexpected afternoon high mixing ratios in Gadanki during the pre-monsoon season. The early morning maximum is high during monsoon and post-monsoon seasons in Dehradun and Gadanki, respectively, whereas morning inflexion occurred earlier in Gadanki compared with Dehradun. The effect of cloudiness on the CO ₂ uptake depends on the canopy cover.