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Trends in Temperature Data: Micro-foundations of Their Nature

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  • Maria Dolores Gadea
  • Jesus Gonzalo
  • Andrey Ramos

Abstract

Determining whether Global Average Temperature (GAT) is an integrated process of order 1, I(1), or is a stationary process around a trend function is crucial for detection, attribution, impact and forecasting studies of climate change. In this paper, we investigate the nature of trends in GAT building on the analysis of individual temperature grids. Our 'micro-founded' evidence suggests that GAT is stationary around a non-linear deterministic trend in the form of a linear function with a one-period structural break. This break can be attributed to a combination of individual grid breaks and the standard aggregation method under acceleration in global warming. We illustrate our findings using simulations.

Suggested Citation

  • Maria Dolores Gadea & Jesus Gonzalo & Andrey Ramos, 2023. "Trends in Temperature Data: Micro-foundations of Their Nature," Papers 2312.06379, arXiv.org.
  • Handle: RePEc:arx:papers:2312.06379
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    1. Michael E. Mann & Raymond S. Bradley & Malcolm K. Hughes, 1998. "Global-scale temperature patterns and climate forcing over the past six centuries," Nature, Nature, vol. 392(6678), pages 779-787, April.
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    6. Pierre Perron & Eduardo Zorita & Francisco Estrada & Pierre Perron, 2017. "Extracting and Analyzing the Warming Trend in Global and Hemispheric Temperatures," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 711-732, September.
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    9. Kim, Dukpa & Perron, Pierre, 2009. "Unit root tests allowing for a break in the trend function at an unknown time under both the null and alternative hypotheses," Journal of Econometrics, Elsevier, vol. 148(1), pages 1-13, January.
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    Cited by:

    1. C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "Temperature in the Iberian Peninsula: Trend, seasonality, and heterogeneity," Papers 2406.14145, arXiv.org.

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    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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