A background note can be accessed here: India’s Mega Science Vision-2035 for Climate Research Infrastructure
The roadmap emphasises India-specific Earth System Models (ESMs), AI-integrated forecasting, and dedicated observational infrastructure tailored to the subcontinent’s geography. To what extent can this push toward indigenous climate science capabilities reduce structural dependence on global climate datasets and externally developed modelling systems?
The emphasis on indigenous Earth System Models (ESMs), enhanced observational infrastructure, and AI-enabled forecasting signifies an important development in India's climate research architecture. Much of the existing climate knowledge base has emerged from modelling systems developed in European countries and the United States. While these systems have made a significant contribution to advancing climate science, there remains a need to examine the extent to which they adequately capture the complexity of regional climatic processes associated with the Indian monsoon, Himalayan ecosystems, coastal regions, and changing land-use patterns.
In this context, the significance of developing indigenous modelling capabilities lies in improving the relevance of climate information for domestic policy priorities. Such capabilities can strengthen India's ability to generate, interpret, and utilise climate intelligence in sectors such as agriculture, disaster management, water security, and infrastructure planning. They can also deepen scientific understanding of region-specific climate dynamics that are central to India's developmental trajectory.
At the same time, climate change remains a global phenomenon that transcends national boundaries. Therefore, the objective should not be viewed as reducing engagement with international scientific cooperation. The challenge lies in developing robust domestic capabilities while ensuring interoperability with global datasets, modelling standards, and scientific networks. The success of this approach will depend on India's ability to balance scientific self-reliance with continued participation in the broader global climate research ecosystem.
Mega Science Vision-2035 proposes a multi-institutional ecosystem spanning satellites, supercomputing, climate-health observatories, and thematic field campaigns. How well aligned is India’s current scientific governance architecture to manage such long-gestation, cross-sector infrastructure systems?
Mega Science Vision-2035 reflects an important recognition that climate change can no longer be viewed solely through an environmental lens. Its implications extend to food security, water availability, public health, infrastructure resilience, livelihoods, and economic development. Consequently, the Vision proposes a research ecosystem that brings together multiple institutions, disciplines, and sectors to address what is fundamentally a cross-cutting developmental challenge.
India already possesses a considerable institutional base in climate and Earth system sciences through organisations such as the Ministry of Earth Sciences, ISRO, the Department of Science and Technology, and a range of specialised research institutions. Therefore, the question is less about the availability of institutions and more about the mechanisms through which they coordinate with one another. Large-scale scientific infrastructure projects often operate across distinct administrative mandates, funding arrangements, and organisational priorities. As a result, issues relating to data sharing, governance of common assets, and long-term coordination acquire particular significance.
The importance of the Vision lies in its attempt to create institutional arrangements capable of supporting sustained collaboration across ministries, scientific agencies, and sectors. Whether such coordination can be effectively embedded over the long duration required by major scientific infrastructure programmes remains to be seen. Much will depend on how successfully fragmentation and duplication of effort can be addressed within the broader governance architecture.
The framework links large-scale climate research investments to agriculture, public health, urban resilience, and carbon-neutrality planning. How robust are the institutional pathways that translate high-end scientific infrastructure into actionable adaptation outcomes at the state and local level?
The Mega Science Vision-2035 acknowledges that investments in climate research infrastructure derive their significance not only from scientific advancement but also from their capacity to inform decision-making across multiple sectors. The framework seeks to establish stronger linkages between climate science and developmental priorities by connecting research investments with areas such as agriculture, public health, urban resilience, disaster preparedness, and long-term planning.
India has made considerable progress in generating climate information through forecasting systems, early warning mechanisms, and sector-specific advisory services. However, the availability of scientific information does not automatically translate into adaptation outcomes. The challenge lies in understanding how climate knowledge can be systematically integrated into decision-making processes at the state and local levels. Variations in institutional capacity, fragmented governance arrangements, and limited interfaces between scientific institutions and administrative agencies continue to affect this process.
In this regard, the Vision’s emphasis on interdisciplinary research, climate-health observatories, resilience studies, and cross-sectoral collaboration is particularly relevant. There is also a need to examine whether institutional mechanisms exist to translate scientific outputs into actionable policy interventions. Going forward, India may need to strengthen dedicated climate service frameworks that can function as intermediaries between climate science and governance institutions. The effectiveness of the proposed investments will ultimately be assessed by the extent to which they strengthen adaptive capacity and resilience in regions most vulnerable to climate impacts.