1-May-2023: Geomagnetic pearl oscillations increase in the recovery phase of geomagnetic storms
Researchers have traced a very significant increase in special continuous oscillations with pearl-type structures called Geomagnetic Pc1 pearl oscillations on the surface of the Earth in the recovery phase of geomagnetic storms. This study is significant for investigating of precipitation particles during geomagnetic storms and can help us understand the radiation hazard to satellites and astronauts.
Earth’s magnetic field forms a protective shield around us, and various plasma waves are generated in this magnetic field cavity. However, geomagnetic storms often cause a dent in this protection. Energetic particles are either accelerated or lost from the Earth’s radiation belts during these storms. This is responsible for changes in plasma environment leading to growth of low-frequency waves called Electromagnetic ion-cyclotron (EMIC) wave instability which is seen as the magnetic field oscillations (0.1-5 Hz) called as Pc1 pulsations.
The Geomagnetic Pc1 pearl oscillations are amplitude-modulated structured narrow-band signals, which are signatures of low-frequency EMIC waves generated by resonant wave-particle interactions in the Earth’s magnetosphere. The observation of these oscillations is a proxy for the measurement of particle precipitation in the Earth’s magnetosphere.
Evidences of these pulsations are abundant in the mid and high-latitude regions. However, at very low latitude stations, it is not frequent. These waves are an important component of space weather in the near-Earth environment.
A team of scientists at IIG, an autonomous institute of the Department of Science and Technology, along with different Indian and global organization investigated the long-term variability of these pulsations in connection to solar cycles 20-21 and descending phase of solar cycle 24 from very low latitude regions of India.
In the study published in the Journal of Atmospheric and Solar-Terrestrial Physics, the researchers used 13 years of archived records covering solar cycle 20–21 from the equatorial site Choutuppal (CPL, L = 1.03) and 5 years of digital induction coil magnetometer data covering descending phase of solar cycle 24 from the low latitude site Desalpar (DSP, L = 1.07) to investigate the structures of Pc1 waves. The morphological changes during quiet and active geomagnetic conditions were investigated, and the role of ionosphere in bringing the high-latitude EMIC wave to low latitude via ionosphere was modelled.
A clear increase in the number of Pc1 at night was observed compared to the day. This is because the attenuation of Pc1 waves upon propagation via the ionospheric waveguide towards lower latitudes is weaker during night hours. Similarly, during the solar maximum period, the transmission rate of Pc1 waves to the equator was diminished than during the solar minimum. The annual and seasonal patterns of Pc1 occurrence showed an inverse relation with sunspot numbers at both stations. An association of these pulsations with active geomagnetic conditions showed the occurrence of Pc1 increasing significantly in the recovery phase of geomagnetic storms.
The understanding of radiation hazards to satellites and astronauts offered by the study is a great necessity in an era highly dependent on satellite-based communication systems.
29-Apr-2023: Newly developed modern analogue dataset based on biotic and abiotic proxy records could be accurate reference tool for the palaeo-ecological studies in the CGP
Scientists have developed a modern analogue dataset based on biotic and abiotic proxy records from different depositional settings like lakebeds, river beds, forest floors, and croplands across two interfluves of the Central Ganga Plain (CGP) that would be an accurate reference tool for the palaeo-ecological studies in the CGP.
The Central Ganga Plain serves as a food basket for thickly populated India and is undergoing significant upheavals in terms of climatic (monsoonal) variability in recent decades. Future scenario assessment requires rigorous climate models which are built utilizing key data inputs (of this eco-system) emerged from well-dated Palaeo-reconstructions.
A considerable number of records are available from the Central Ganga Plain with restricted information on Palaeo-environmental reconstruction. Modern proxies to distinguish different ecology and depositional environment at the appropriate spatial scale are limited, and generation of such proxies is vital for decoding the past environment in the CGP.
Furthermore, the Ghaghara-Gandak and Ganga-Ghaghara interfluve regions are areas where several meter-thick sediments have been deposited during the Late Quaternary. The interfluve regions are comprised of different depositional environments, such as fluvial, lacustrine, forest, and croplands, so they are important for past environmental and modern analogue studies. The soil/sediment samples can be complemented with biotic (pollen, diatoms, and phytoliths) and abiotic proxies (sediment texture, stable carbon and nitrogen isotopes, XRD/ XRF elements, and magnetic susceptibility parameters).
The BSIP, an autonomous institute of DST, evaluated the strength and weaknesses of biotic and abiotic proxy records of the Ghaghara-Gandak and Ganga-Ghaghara interfluves of CGP.
For the first time, they adopted a holistic approach towards developing multiproxy modern analogues from the two interfluves, which would be an accurate reference tool for the palaeo-ecological studies in the Central Ganga Plain and surrounding areas. The study published in the journal Catena, evaluated both the strength and weaknesses of these proxies and assessed how reliably multiproxy modern analogues can identify different ecological and depositional environments and could be used as a baseline in interpreting Late Quaternary palaeo-environmental and ecological changes more accurately in this region.
The study of biotic and abiotic interactions is important as they aid in building the forest community, food crops, agro-pastoral and human settlements in this region. Consequently, the palaeo-ecological data would assist in better understanding the past and also the sustainable future projections in the Central Ganga Plain.
For example, the inception of human settlement in this region could also be traced through the establishment of marker pollen, phytolith, and diatom taxa. The high/low occurrences of annual herbs like Euphorbiaceae and Convolvulaceae (marker pollen taxa) indicated the monsoonal fluctuation in the Central Ganga Plain. Besides, the different cultural pollen taxa apprised how human-associated changes have reduced the forest cover in the CGP, and hence those forest trees should be plated that can generate and sustain our life-supporting system by giving out oxygen and also combat the rising CO2 levels by carbon sequestration.
The work stands out due to the fact that the fossil pollen represents the plant upto species level and hence could directly trace vegetation changes, and pollen could be an accurate tool for monitoring the large-scale variability in climate change scenarios.
The study would help measure the dynamics of the natural vegetation and the shifts in human occupation over time for future scenario development. This modern comprehensive dataset could provide background information for the Late Quaternary palaeo-ecological reconstruction from the Central Ganga Plain along with the practices for preserving and conserving the endangered biodiversity that flourishes in forests, crops, lakes, and rivers system of this region.
The lakes of the CGP, which were once proliferated with water and supported human settlement, are presently drying up and need to be preserved and cleaned so that the rich biodiversity flourishing in and around the lakes could be used for sustainable future development. Hence the various proxies used in this study helps in generating eco-environmental prospect of wetland and sediments status in this region.
The multiparameter study could also be viewed as an important baseline for conserving different lakes and river systems, often treated as wastelands to be drained, filled, and converted for other purposes.
26-Apr-2023: AI helps improve predictability of Indian Summer Monsoons
A newly devised algorithm powered by Artificial Intelligence can help increase the predictability of the Indian Summer Monsoons (ISMR) 18 months ahead of the season. The algorithm called predictor discovery algorithm (PDA) made using a single ocean-related variable could facilitate skillful forecast of the ISMR in time for making effective agricultural and other economic plans for the country.
While researchers have well established the scientific basis for ISMR predictability and made significant advances over the past century in understanding the variability and predictability of ISMR, the skillful prediction of ISMR even one month in advance has remained a major challenge. Neither the potential (theoretically possible) skill (correlation between the predicted and observed ISMR) and the actual skill of ISMR forecast are available at longer lead times--6, 12, 18, 24- months ahead of the season.
Traditionally, researchers select a predictor of ISMR based on the maximum correlation of an atmospheric or oceanic variable with ISMR over a region of the globe. Such technique restricts in the realization of the true potential predictability of ISMR as it accounts for one predictor over a particular region at a time.
Scientists at the Institute of Advanced Study in Science and Technology (IASST), Guwahati, an autonomous institute of DST along with their collaborators have found that the widely used sea surface temperature (SST) is inadequate for calculation of long-lead prediction of ISMR. This, they found was because the potential skill of ISMR estimated by the predictor discovery algorithm (PDA) using SST-based predictors was low at all the lead months.
The team consisting of IASST Indian Institute of Tropical Meteorology (IITM), Pune, and Cotton University, Guwahati, devised a predictor discovery algorithm (PDA) that generates predictor at any lead month by projecting the ocean thermocline depth (D20) over the entire tropical belt between 1871 and 2010 onto the correlation map between ISMR and D20 over the same period.
This encapsulated all the potential drivers over the entire tropical region in order to realize the true potential skill of ISMR prediction at any lead month. This is because this one predictor embedded the simultaneous contribution of all the potential drivers over the entire tropical belt at any lead month identified by the correlation map. Besides, the oceanic thermocline depth (D20) is least influenced by the stochastic atmospheric noise.
The new algorithm indicates that the potential skill of ISMR is maximum (0.87, highest being 1.0) 18-months before the ISMR season. At any lead month, the predictability of the annual variability of ISMR depends on the degree of regularities in the annual variability of its drivers.
With the newly discovered basis of long-lead ISMR predictability in place, Devabrat Sharma (IASST), Dr. Santu Das (IASST), Dr. Subodh K. Saha (IITM), and Prof. B. N. Goswami (Cotton University) were able to make 18-months lead forecast of ISMR between 1980 to 2011 with an actual skill of 0.65 using a machine learning based ISMR prediction model. The success of the model was based on the ability of artificial intelligence (AI) to learn the relationship between ISMR and tropical thermocline patterns from 150 years of simulations by 45 physical climate models and transferring that learning to actual observations between 1871 and 1974. As the potential skill of ISMR at 18-months lead is 0.87, there is still considerable scope in improving the model.
The findings published in Quarterly Journal of the Royal Meteorological Society paves way for long-lead skilful prediction of ISMR in coming years with accelerating improvement of coupled climate models together with the advent of nonlinear machine learning tools. Skillful long-lead forecasts of ISMR like one year ahead of the season will be highly beneficial to the policymakers and farmers in planning and making the country’s food production resilient to increasing vagaries of ISMR with global warming.