Iron limitation study in the North Indian Ocean using model simulations
by M K Sharada, C Kalyani Devasena and P S Swathi
There have been many studies on iron as a limiting nutrient for productivity in the World Ocean, but only a few studies have been done in the Arabian Sea on iron limitation. Sensitivity of primary productivity, chlorophyll, nitrate, pCO2 and carbon Cux in the Arabian Sea to one of the parameters related to iron limitation has been investigated using a three-dimensional coupled biogeochemical model (TOPAZ) embedded in Modular Ocean Model (MOM) in the global domain, for climatology and interannual variability. Initially, the model results are evaluated for many of the biogeochemical components using data from World Ocean Atlas, satellites and cruises in the Arabian Sea. It is noticed that model results capture spatial and temporal variations of some of the biogeochemical components and Cuxes in the north Indian Ocean. Subsequently, it is shown that micronutrient iron plays a significant role on the growth of phytoplankton, utilization of nutrients, pCO2 in the ocean and carbon Cux across the air–sea interface in north and north-west regions of the Arabian Sea.
Source: https://link.springer.com/article/10.1007%2Fs12040-020-1361-9
Investigation of background noise in the GNSS position time series using spectral analysis – A case study of Nepal Himalaya
by Jagat Dwipendra Ray, M. Sithartha Muthu Vijayan, Walyeldeen Godah, Ashok Kumar
Position time series from permanent Global Navigation Satellite System (GNSS)stations are commonly used for estimating secular velocities of discrete points on the Earth’s surface. An understanding of background noise in the GNSS position time series is essential to obtain realistic estimates of velocity uncertainties. The current study focuses on the investigation of background noise in position time series obtained from thirteen permanent GNSS stations located in Nepal Himalaya using the spectral analysis method. The power spectrum of the GNSS position time series has been estimated using the Lomb–Scargle method. The iterative nonlinear Levenberg–Marquardt (LM) algorithm has been applied to estimate the spectral index of the power spectrum. The power spectrum can be described by white noise in the high frequency zone and power law noise in the lower frequency zone. The mean and the standard deviation of the estimated spectral indices are 1.46±0.14; 1.39±0.16 and1.53±0.07 for north, east and vertical components, respectively. On average, the power law noise extends up to a period of ca. 21 days. For a shorter period, i.e. less than ca. 21days, the spectra are white. The spectral index corresponding to random walk noise (ca. –2) is obtained for a site located above the base of a seismogenic zone which can be due to the combined effect of tectonic and nontectonic factors rather than a spurious monumental motion. Overall, the usefulness of investigating the background noise in the GNSS position time series is discussed.
Ray, J. D., M. S. M. Vijayan, W. Godah, and A. Kumar (2019), Investigation of background noise in the GNSS position time series using spectral analysis – A case study of Nepal Himalaya, Geod. Cartogr., 68(No 2), 375–388, doi:10.24425/gac.2019.128468
Full Text: http://journals.pan.pl/dlibra/publication/128468/edition/112071/content
Intensity duration and frequency of Heat wave in different phases of MJO over India
by Rani Devi, K C Gouda and Smrutishree Lenka
The Madden-Julian Oscillation (MJO) is an important tropical atmospheric phenomenon that plays a significant role in modulating weather patterns across India. Heat Waves (HW) are extreme weather events that have severe impacts on human health, agriculture, and various socio-economic sectors. This research paper aims to investigate the dynamical relationship between MJO phases, as measured by the Real-time Multivariate MJO (RMM) index, and the occurrence of HW in India, as defined by the Indian Meteorological Department. This paper demonstrated the relationship among three aspects, i.e., intensity, duration, frequency of HW during the different MJO phases. The differential dynamics associated for about two-fold rise in the HW occurrence in the South East, Eastern and North West India in the MJO active phase. Inactive phase witnessed enhancement (suppression) of HW during wet (dry) over the regions. The novel finding of this paper is that, the weaker the MJO, the higher probability of HW occurrence in respective regions depending on the position of the MJO convection center. The maximum duration is observed to be significantly decreased by about 2 days in the active MJO phase over NW and north central parts of the country. It is also inferred from this study that there is a significantly decrease of 0.5 °C in HW intensity during the active wet phase of MJO over the NW India. To explain the linkage of HW with MJO, geopotential height (GPH), wind circulation, velocity potential (VP), sea surface temperature (SST) and outgoing longwave radiation (OLR) composite anomalies have been computed and used in the analysis. It is observed that all these parameters have a direct impact on the evolution, strength and spread of the HW in India. This dynamical study mechanism can be prescribed in the numerical weather prediction models for the improved prediction of such extreme weather events during summer for proactive disaster management.
Insights from a Pan India Sero-Epidemiological survey (Phenome-India Cohort) for SARS-CoV2
by Salwa Naushin, Viren Sardana, Rajat Ujjainiya, Nitin Bhatheja, Rintu Kutum, Akash Kumar Bhaskar, Shalini Pradhan, Satyartha Prakash, Raju Khan, Birendra Singh Rawat, Karthik Bharadwaj Tallapaka, Mahesh Anumalla, Giriraj Ratan Chandak, Amit Lahiri, Susanta Kar, Shrikant Ramesh Mulay, Madhav Nilakanth Mugale, Mrigank Srivastava, Shaziya Khan, Anjali Srivastava, Bhawna Tomar, Murugan Veerapandian, Ganesh Venkatachalam, Selvamani Raja Vijayakumar, Ajay Agarwal, Dinesh Gupta, Prakash M Halami, Muthukumar Serva Peddha, Gopinath M Sundaram, Ravindra P Veeranna, Anirban Pal, Vinay Kumar Agarwal, Anil Ku Maurya, Ran Vijay Kumar Singh, Ashok Kumar Raman, Suresh Kumar Anandasadagopan, Parimala Karuppanan, Subramanian Venkatesan, Harish Kumar Sardana, Anamika Kothari, Rishabh Jain, Anupma Thakur, Devendra Singh Parihar, Anas Saifi, Jasleen Kaur, Virendra Kumar, Avinash Mishra, Iranna Gogeri, Geethavani Rayasam,
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