2,3,4,5,11,12- 1National Taiwan Normal University, Graduate Institute of Sustainability Management and Environmental Education, Taipei, Taiwan (khelin@ntnu.edu.tw)
- 2School of Applied Sciences, University of Brighton, Brighton, UK
- 3School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, South Africa
- 4Department of Geography, King’s College London, Strand, London, UK
- 5Department of Geography, Faculty of Science, Masaryk University, Brno, Czech Republic
- 6Fenner School of Environment & Society, Australian National University, Australia
- 7Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, Florida, USA
- 8Department of History, Åbo Akademi University, Finland
- 9Argentine Institute of Nivology, Glaciology and Environmental Sciences (IANIGLA-CONICET), Mendoza, Argentina
- 10Facultad de Filosofía y Letras, Universidad Nacional de Cuyo, Mendoza, Argentina
- 11Institute of Sustainable Development and Climate Policy, National Tsing Hua University, Hsinchu, Taiwan
- 12Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
The field of historical climate reconstruction through documentary evidences has made significant advances in recent years, including a global synthesis of the index approach in climate reconstruction (Nash et al., 2021), a global documentary climate dataset (Burgdorf et al., 2023), and new perspectives to study historical climatology (White et al., 2022). Based on the progress, this study addresses two specific research questions: 1) What are the numerical structures and characteristics of the reconstructed climate index data across existing datasets from various continents? (2) Are these structures and characteristics reflective of long-term climatological traits in the regions, or are they controlled by other factors? By answering the questions, we collected climate index data from Africa, Australia, China, Europe, India and South America to analyze the data structure in each dataset. Each dataset’s structure was then compared against modern observational data, including ERA5 and the Global Historical Climatology Network (GHCN) monthly data, to identify correlations and asses credibility of the climate index data. The analysis involves spatial and temporal comparisons, statistical tests, and discussions on data quality and limitations.
How to cite: Lin, K.-H. E., Lin, Y.-H., Lee, J.-I., Nash, D., Grab, S., Adamson, G. C. D., Dobrovolný, P., Gergis, J., Nicholson, S. D., Norrgård, S., del Rosario Prieto, M., Tseng, W.-L., and Huang, H.-C.: A global synthesis of climate indices reconstructed from historical archives, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6141, https://doi.org/10.5194/egusphere-egu26-6141, 2026.
