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The role of global reanalyses in climate services for health: insights from the Lancet Countdown

Di Napoli, C. ORCID:, Romanello, M., Minor, K. ORCID:, Chambers, J. ORCID:, Dasgupta, S. ORCID:, Escobar, L. E. ORCID:, Hang, Y. ORCID:, Hänninen, R. ORCID:, Liu, Y. ORCID:, Lotto Batista, M. ORCID:, Lowe, R. ORCID:, Murray, K. A. ORCID:, Owfi, F. ORCID:, Rabbaniha, M. ORCID:, Shi, L. ORCID:, Sofiev, M. ORCID:, Tabatabaei, M. ORCID: and Robinson, E. J. Z. ORCID: (2023) The role of global reanalyses in climate services for health: insights from the Lancet Countdown. Meteorological Applications, 30 (2). e2122. ISSN 1469-8080

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To link to this item DOI: 10.1002/met.2122


As the linkages between extreme weather events, changes in climatic conditions and health impacts in exposed populations become clearer, so does the need for climate-smart decisions aimed at making the public health sector more responsive and resilient. By integrating climate and health information, climate services for health provide robust decision-support tools. The Lancet Countdown monitoring system uses global climate reanalyses products to track annual changes in a set of health-related outcomes. In the monitoring system, multiple variables from reanalysis datasets such as ERA5 and ERA5-Land are retrieved and processed to capture heatwaves, precipitation extremes, wildfires, droughts, warming and ecosystem changes across the globe and over multiple decades. This reanalysis-derived information is then input into a hazard–exposure–vulnerability framework that delivers, as outcomes, indicators tracking the year-by-year impacts of climate-related hazards on human mortality, labour capacity, physical activity, sentiment, infectious disease transmission, and food security and undernutrition. Building on the reanalysis gridded format, the indicators create worldwide ‘maps without gaps’ of climate–health linkages. Our experience shows that reanalysis datasets allow standardization across the climate information used in the framework, making the system potentially adaptable to multiple geographical scales. An ongoing challenge is to quantify how the inherent bias of global reanalyses influences indicator outcomes. We foresee the health sector as a key user of reanalysis products. Therefore, public health professionals and health impact modellers should be involved in the co-development of future iterations of reanalysis datasets, to reach finer spatial resolutions and provide a wider set of health-relevant climate variables.

Item Type:Article
Divisions:Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science
Life Sciences > School of Agriculture, Policy and Development > Department of Agri-Food Economics & Marketing
ID Code:111369
Publisher:Royal Meteorological Society


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