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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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High-Throughput Assays of Critical Thermal Limits in Insects
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Published on: June 15, 2020

Statistical properties of record-breaking temperatures.

William I Newman1, Bruce D Malamud, Donald L Turcotte

  • 1Department of Earth and Space Sciences, University of California, Los Angeles, California 90095, USA. win@ucla.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|January 15, 2011
PubMed
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Global warming trends and long-range correlations minimally impact temperature record-breaking statistics, influencing them by less than 10%. Warming trends are mainly driven by rising minimum temperatures, not maximum temperatures.

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Area of Science:

  • Climate Science
  • Statistical Physics
  • Time Series Analysis

Background:

  • Temperature time series analysis is crucial for understanding climate change.
  • Record-breaking temperatures (extremes) are key indicators of climate variability and trends.
  • Previous theories often assumed independent and identically distributed (i.i.d.) data, neglecting trends and correlations.

Purpose of the Study:

  • To investigate the influence of temperature trends and long-range correlations on record-breaking statistics.
  • To develop a theoretical framework for analyzing record-breaking events in non-stationary time series.
  • To validate the model using real-world temperature data.

Main Methods:

  • Monte Carlo simulations were employed to model temperature time series.
  • Fractional Gaussian noise was used to represent long-range correlations.
  • Linear trends were superimposed on Gaussian white noise to simulate warming.
  • Analysis of record-breaking temperature ratios (maximum vs. minimum) was performed.

Main Results:

  • Long-range correlations and linear trends had a minor influence (<10%) on record-breaking statistics for typical temperature time series.
  • A single governing parameter, the ratio of annual temperature change to noise standard deviation, was identified for trend analysis.
  • Analysis of Mauna Loa Observatory data (1977-2006) showed good agreement between direct trend measurement and simulation-inferred trends.
  • The observed warming trend was primarily attributed to an increase in minimum temperatures, while maximum temperatures remained relatively stable.

Conclusions:

  • The statistical properties of record-breaking temperatures are robust to moderate trends and long-range correlations.
  • The developed theory provides a reliable method for inferring temperature trends from extreme temperature events.
  • The study highlights that diurnal temperature range changes are a significant factor in observed warming trends.