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Testing extreme warming and geographical heterogeneity

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  • Gadea Rivas, María Dolores
  • Gonzalo, Jesús
  • Olmo, José

Abstract

Extreme climate events represent a critical climate risk and a global challenge. Understanding the heterogeneity in worldwide temperatures is crucial for predicting future climate change dynamics and guiding effective policy responses. This paper addresses both issues by analyzing the presence of trends in the tail decay of the realized distribution of annual temperatures for eight regions spanning the globe over the period 1960 to 2022. Our empirical findings reveal that extreme warming exhibits heterogeneity across regions and seasons, with a pronounced distinction between the Northern and Southern hemispheres. Importantly, extreme warming predominantly occurs in both hemispheres during the period from June to September.

Suggested Citation

  • Gadea Rivas, María Dolores & Gonzalo, Jesús & Olmo, José, 2024. "Testing extreme warming and geographical heterogeneity," UC3M Working papers. Economics 45023, Universidad Carlos III de Madrid. Departamento de Economía.
    • Handle: RePEc:cte:werepe:45023
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    References listed on IDEAS

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    1. Laurens de Haan, 1976. "Sample extremes: an elementary introduction," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 30(4), pages 161-172, December.
    2. Gadea Rivas, María Dolores & Gonzalo, Jesús, 2020. "Trends in distributional characteristics: Existence of global warming," Journal of Econometrics, Elsevier, vol. 214(1), pages 153-174.
    3. Igor Fedotenkov, 2020. "A Review of More than One Hundred Pareto-Tail Index Estimators," Statistica, Department of Statistics, University of Bologna, vol. 80(3), pages 245-299.
    4. de Haan, Laurens, 1976. "Sample Extremes: An Elementary Introduction," Econometric Institute Archives 272130, Erasmus University Rotterdam.
    5. Einmahl, J. H.J. & Dekkers, A. L.M. & de Haan, L., 1989. "A moment estimator for the index of an extreme-value distribution," Other publications TiSEM 81970cb3-5b7a-4cad-9bf6-2, Tilburg University, School of Economics and Management.
    6. Chang, Yoosoon & Kim, Chang Sik & Park, Joon Y., 2016. "Nonstationarity in time series of state densities," Journal of Econometrics, Elsevier, vol. 192(1), pages 152-167.
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