Evaluating the role of observational uncertainty in climate impact assessments: Temperature-driven yellow fever risk in South America

Sally Jahn , Keith Fraser, Katy AM Gaythorpe, Caroline M Wainwright, Neil M Ferguson

Global gridded temperature data sets (GGTDs) vary in their information sources,
quality control procedures, generation techniques, and spatial-temporal resolutions,
introducing observational uncertainty. This uncertainty is critical not only for studies
on current climate conditions but also for future climate change projections, where
observational data sets are used for bias correction and downscaling of global climate
model (GCM) outputs. To minimize the impact of biases on current assessments and
future projections, it is essential to ensure that the reference data set accurately
represents the true climate state and spans a sufficiently long period to filter out
internal variability. The selection of appropriate GGTDs is hence a crucial yet often
overlooked factor in research that examines the impact of climate variability and change
on vector-borne diseases such as yellow fever (YF). YF, an arboviral disease endemic to
tropical regions of Africa and South America, has transmission dynamics that may be
significantly influenced by climate change. In this study, we evaluated four GGTDs,
namely the Berkeley Earth Surface Temperatures (BEST), the Climatic Research Unit
Time-Series (CRUTS) and the ERA5 and ERA5Land reanalysis data sets, for
health-related impact research, specifically examining YF transmission in South
America. Each dataset was evaluated via grid-based analysis and validated against
national weather station data, focusing on Brazil and Colombia, where YF outbreak risk
remains. Our findings show that substantial differences among GGTDs affect the
spatial representation of climate change indices, bioclimatic variables, and spatially
aggregated temperature estimates at the administrative unit level, which generally serve
as inputs for transmission models. In particular, while the reanalysis data sets generally
outperformed the lower-resolution products, ERA5 demonstrated a slight advantage
over ERA5Land despite the latter’s higher spatial resolution. Most importantly, our
results highlight that variations among GGTDs can lead to markedly different estimates
of key disease transmission parameters.