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Related Concept Videos

Global Climate Change01:50

Global Climate Change

Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.
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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
What is Climate?01:16

What is Climate?

Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.
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Precipitation Processes

The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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Regression Analysis

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Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...

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Related Experiment Video

Updated: Jun 3, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Inferring long memory processes in the climate network via ordinal pattern analysis.

Marcelo Barreiro1, Arturo C Marti, Cristina Masoller

  • 1Instituto de Física, Facultad de Ciencias, Universidad de la República, Iguá, 4225, Montevideo, Uruguay.

Chaos (Woodbury, N.Y.)
|April 5, 2011
PubMed
Summary
This summary is machine-generated.

This study reveals that global climate networks exhibit repeating oscillatory patterns. These patterns explain surface air temperature variability on intraseasonal to interannual timescales, including El Niño events.

Related Experiment Videos

Last Updated: Jun 3, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Climate Science
  • Network Theory
  • Time Series Analysis

Background:

  • Understanding climate variability is crucial for predicting future climate change.
  • Global climate networks offer a novel framework for analyzing complex climate dynamics.
  • Identifying memory processes in climate data is key to understanding its predictability.

Purpose of the Study:

  • To construct global climate networks using surface air temperature data.
  • To uncover long- and short-term memory processes within these networks.
  • To identify the oscillatory behaviors driving climate variability.

Main Methods:

  • Ordinal patterns and symbolic analysis were employed.
  • Monthly averaged surface air temperature (SAT) fields were analyzed.
  • Network construction was used to represent climate system interactions.

Main Results:

  • The time variability of the SAT field is characterized by repeating oscillatory patterns.
  • These oscillations exhibit periodicity related to intraseasonal oscillations.
  • El Niño events on seasonal-to-interannual timescales were linked to these patterns.

Conclusions:

  • Climate variability is governed by recurring oscillatory dynamics.
  • Symbolic analysis of climate networks effectively reveals memory processes.
  • The findings enhance our understanding of climate predictability on multiple timescales.