In the twenty-first century, machine learning and deep learning have been successfully used to find hidden information from coarse-grained data in various domains. In Computer Vision, scientists have used neural networks to identify hidden pixel-level information from low-resolution (LR) image data. This approach of estimating high-resolution (HR) information from LR data is called the super-resolution (SR) approach. This approach has been borrowed by climate scientists to downscale coarse-level measurements of climate variables to obtain their local-scale projections. Climate variables are spatial in nature and can be represented as images where each pixel denotes a grid point where the variables can be measured. We can apply the deep learning-based SR techniques on such “images” for statistical downscaling of such variables. This approach of downscaling can be termed as deep downscaling. In this work, we have tried to make HR projection of the Indian summer monsoon rainfall by using a novel deep residual network called ResDeepD. The aim is to downscale the 10 × 10 low LR precipitation data to get the values at 0.250 × 0.250 resolution. The proposed model uses a series of skip connections across residual blocks to give better results as compared to the existing models like super-resolution convolutional neural network, DeepSD, and Nest-UNet that have been used previously for this task. We have also examined the model’s performance for downscaling rainfall during some extreme climatic events like cyclonic storms and deep depression and found that the model performs better than the existing models.

We have analysed a 6100-year record of benthic and planktonic foraminifera from inner neritic sediments from Core SK291/GC13, off the Goa coast, eastern Arabian Sea, to understand the response of benthic foraminifera to shallow-marine processes. The benthic foraminiferal assemblage is dominated by Nonion cf. asterizans, Ammonia beccarii, A. gaimardii and Virgulinella fragilis, which have been selected on the basis of a population of 10% or more in any three samples analysed. The planktonic foraminiferal population is sporadic and rare, with Globigerinoides ruber as the predominant species showing a variable trend. The foraminiferal proxies combined with total organic carbon (wt%) and δ13C and δ18O values of Ammonia gaimardii suggest distinct variations, indicating changes in productivity and salinity in the shallow eastern Arabian Sea. The coastal waters off Goa were relatively warmer and less saline between 6100 and 4600, or perhaps to 4200, calibrated years before the present (cal yr BP), corresponding to a stronger monsoon in South and East Asia. The shallow sea was cooler from ~4200 to 2600 cal yr BP in the study area, coinciding with a lower sea surface temperature in the northeastern Arabian Sea and an arid phase in the Indian subcontinent. From 2900 to 2600 cal yr BP the study core exhibits the impacts of short-term cold events, which have earlier been observed in the northeastern Arabian Sea, off Pakistan. During the Little Ice Age, the shallow sea off Goa was less productive.

Variations in fossil diatom assemblages and their relationship with global and Indian monsoon climate changes for the last 600,000 yr were investigated using a core of ancient lake (Paleo-Kathmandu Lake) sediments drilled at the Kathmandu Basin, Nepal Himalaya. Chronological scales of the core were constructed by tuning pollen wet and dry index records to the SPECMAP δ18O stack record. Examinations of biogenic silica contents and fossil diatom assemblages revealed that variations in productivity and compositions of diatom assemblages were closely linked with global and Indian monsoon climate changes on glacial and interglacial time scales. When summer monsoonal rainfall increased during interglacials (interstadials), diatom productivity increased because of increased inputs of terrestrial nutrients into the lake. When summer monsoonal rainfall reduced and/or winter monsoonal aridification enhanced during glacials (stadials), productivity of the diatoms decreased and lake-level falling brought about changes in compositions of diatom assemblages. Monospecific assemblages by unique Cyclotella kathmanduensis and Puncticulata versiformis appeared during about 590 to 390 ka. This might be attributed to evolutionary fine-tuning of diatom assemblages to specific lake environmental conditions. Additionally, low-amplitude precessional variations in monsoon climate and less lake-level changes may have also allowed both species to dominate over the long periods.

The Diancang Massif is located in a region linking the Tibetan and Yungui Plateaus. Climatically, it is in a transition belt between the south and middle subtropical zones, controlled by Indian monsoon and westerlies. Thus, this study provides more evidences on the evolution of Indian monsoon since the Holocene. We reconstruct the history of climate on the Diancang Massif since 11.5 ka, using integrated correlation of glacial activities, early human settlement sites, and climate proxies abstracted from variations in grain size, magnetic susceptibility, geochemical composition, and pollen in lacustrine sediments. Six climatic stages have been identified. Stage I, from 11.5 ka to 9.0 ka, is a relatively wet period, corresponding to the onset of the Holocene; from 9.5 ka to 6.0 ka, the climate is arid; a cold period follows from 6.0 ka to 5.3 ka, and this is succeeded by a temperate stage from 5.3 ka to 4.0 ka; from 4.0 ka to 0.73 ka the climate is again arid. Compared with other regions dominated by the Indian monsoon, there is a delay in response of the climate on the Diancang Massif to the onset of the Holocene.