Among the tropical oceans, the western Indian Ocean hosts one of the largest concentrations of marine phytoplankton blooms in summer. Interestingly, this is also the region with the largest warming trend in sea surface temperatures in the tropics during the past century—although the contribution of such a large warming to productivity changes has remained ambiguous. Earlier studies had described the western Indian Ocean as a region with the largest increase in phytoplankton during the recent decades. On the contrary, the current study points out an alarming decrease of up to 20% in phytoplankton in this region over the past six decades. We find that these trends in chlorophyll are driven by enhanced ocean stratification due to rapid warming in the Indian Ocean, which suppresses nutrient mixing from subsurface layers. Future climate projections suggest that the Indian Ocean will continue to warm, driving this productive region into an ecological desert.

Uncorrected refractive error is an avoidable cause of visual impairment which affects children in India. The objective of this review is to estimate the prevalence of refractive errors in children ≤ 15 years of age. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in this review. A detailed literature search was performed to include all population and school-based studies published from India between January 1990 and January 2017, using the Cochrane Library, Medline and Embase. The quality of the included studies was assessed based on a critical appraisal tool developed for systematic reviews of prevalence studies. Four population-based studies and eight school-based studies were included. The overall prevalence of refractive error per 100 children was 8.0 (CI: 7.4-8.1) and in schools it was 10.8 (CI: 10.5-11.2). The population-based prevalence of myopia, hyperopia (≥ +2.00 D) and astigmatism was 5.3 per cent, 4.0 per cent and 5.4 per cent, respectively. Combined refractive error and myopia alone were higher in urban areas compared to rural areas (odds ratio [OR]: 2.27 [CI: 2.09-2.45]) and (OR: 2.12 [CI: 1.79-2.50]), respectively. The prevalence of combined refractive errors and myopia alone in schools was higher among girls than boys (OR: 1.2 [CI: 1.1-1.3] and OR: 1.1 [CI: 1.1-1.2]), respectively. However, hyperopia was more prevalent among boys than girls in schools (OR: 2.1 [CI: 1.8-2.4]). Refractive error in children in India is a major public health problem and requires concerted efforts from various stakeholders including the health care workforce, education professionals and parents, to manage this issue.

Sea surface temperature (SST) and upper ocean heat content (OHC, upper 700 m) in the tropical Indian Ocean underwent rapid warming during 1950–2015, with the SSTs showing an average warming of about 1 °C. The SST and OHC trends are very likely to continue in the future, under different emission scenarios. Climate models project a rise in tropical Indian Ocean SST by 1.2–1.6 °C and 1.6–2.7 °C in the near (2040–2069) and far (2070–2099) future across greenhouse gas (GHG) emissions scenarios RCP4.5 and RCP8.5, relative to the reference period of 1976–2005. Indian Ocean warming has very likely resulted in decreasing trend in oxygen (O2) concentrations in the tropical Indian Ocean, and declining trends in pH and marine phytoplankton over the western Indian Ocean. The observed trends in O2, pH and marine phytoplankton are projected to increase in the future with continued GHG emissions.

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Continuous remote-sensed daily fields of ocean color now span over two decades; however, it still remains a challenge to examine the ocean ecosystem processes, e.g., phenology, at temporal frequencies of less than a month. This is due to the presence of significantly large gaps in satellite data caused by clouds, sun-glint, and hardware failure; thus, making gap-filling a prerequisite. Commonly used techniques of gap-filling are limited to single value imputation, thus ignoring the error estimates. Though convenient for datasets with fewer missing pixels, these techniques introduce potential biases in datasets having a higher percentage of gaps, such as in the tropical Indian Ocean during the summer monsoon, the satellite coverage is reduced up to 40% due to the seasonally varying cloud cover. In this study, we fill the missing values in the tropical Indian Ocean with a set of plausible values (here, 10,000) using the classical Monte-Carlo method and prepare 10,000 gap-filled datasets of ocean color. Using the Monte-Carlo method for gap-filling provides the advantage to estimate the phenological indicators with an uncertainty range, to indicate the likelihood of estimates. Quantification of uncertainty arising due to missing values is critical to address the importance of underlying datasets and hence, motivating future observations.