Tom Bearpark

Rainfall and flooding frequently disrupt the lives of urban residents worldwide, posing significant public health risks. Rapid urbanisation is exposing larger and more vulnerable populations to these hazards, while climate change intensifies rainfall patterns, and rising sea levels impair drainage systems. Despite the growing recognition and urgency of these hazards, the health impacts of rainfall remain poorly understood, and those of sea level rise are entirely unquantified. Without robust quantification, we lack a complete understanding of the health risks posed by extreme weather and climate change, particularly in rapidly growing megacities where vulnerable populations are disproportionately affected. Here, we estimate the mortality consequences of rainfall in one of the world’s largest cities – Mumbai, India. We integrate high-resolution data on rainfall, tides, and mortality, to analyse how extreme rainfall and its interaction with tidal dynamics contribute to urban health risks. We find that rainfall causes roughly 8% of Mumbai’s deaths during the monsoon season, and that nearly 90% of this burden is borne by slum-residents. We also find that children face the biggest increase in mortality riskfrom rainfall, and women face a greater risk than men. Additionally, we demonstrate that mortality risk from rainfall increases sharply during high tides and use this relationship to project how rising sea levels will amplify rainfall-induced mortality in the future. Our findings reveal that the mortality impacts of rainfall are an order of magnitude larger than is documented by official statistics, highlighting the urgent need for investment in improved drainage, sanitation, and waste management infrastructure, particularly in cities in the Global South. Moreover, our analysis highlights that the health costs of extreme rainfall and sea level rise are a critical omission in current projections of climate change impacts.

Quantifying and projecting climate migration is a critical challenge for policymakers and researchers. While causal inference, agent-based, gravity, and general equilibrium models have been employed widely to estimate the climate-migration relationship, their results often diverge, and they are typically used in isolation and at different spatio-temporal scales. Here, we aim to bridge this gap by introducing a unified notation and conceptual framework, designed to compare and evaluate the assumptions and outputs of these diverse models at a common scale. We illustrate our comparison using an empirically relevant case study: estimating and projecting the relationship between annual average temperature and internal state-to-state migration in the United States. We analyse results from hundreds of model permutations, illustrating how methodological choices and researcher degrees of freedom shape model outcomes. Our findings show that variations in modelling approach introduce uncertainty comparable in magnitude to statistical uncertainty. By integrating policy-relevant insights across methods, we highlight the value of model intercomparison in improving projections and informing policy decisions.

Empirical climate impacts research is dominated by studies that use panel fixed-effects regressions to estimate the causal effect of weather on outcomes. However, the functional form of time controls in these studies, i.e., the specification of time trends, lacks a clear theoretical foundation or data-driven justification. Moreover, these choices can substantially influence the magnitude of estimated results in important applications. In this paper, we elucidate a framework for choosing time controls in climate impacts studies. We propose a two-equation model which aligns with the reasoning used in climate impacts studies to justify identification. In our framework, competing models are evaluated according to their ability to isolate plausibly random variation in the treatment variable. We provide simulations to illustrate our proposal, and apply our framework to an open and policy relevant agenda in the climate impacts literature; estimating the effect of temperature on GDP growth rates.