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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Uncertainty: Confidence Intervals00:54

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Propagation of Uncertainty from Systematic Error01:10

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Propagation of Uncertainty from Random Error00:59

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Random Error01:04

Random Error

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Global Climate Change01:50

Global Climate Change

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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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Uncertainty maps for model-based global climate classification systems.

Andrés Navarro1, Andrés Merino2, Eduardo García-Ortega2

  • 1Earth and Space Sciences (ESS) Group, Institute of Environmental Sciences, University of Castilla-La Mancha (UCLM), Avda. Carlos III s/n, 45071, Toledo, Spain. andres.navarro@uclm.es.

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Climate classification systems (CCSs) derived from global climate models (GCMs) have limitations. This study maps uncertainties in four CCSs using CMIP6 data, aiding climate change adaptation and mitigation efforts.

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Area of Science:

  • Climate Science
  • Environmental Science

Background:

  • Climate classification systems (CCSs) are vital for climate change studies, but their limitations, especially when using Global Climate Model (GCM) outputs, are often underestimated.
  • Non-specialists frequently misunderstand the inherent uncertainties in CCSs, leading to potential misinterpretations in climate change mitigation and adaptation planning.

Purpose of the Study:

  • To address the misunderstanding of CCS limitations by providing uncertainty maps for widely used classification schemes.
  • To offer guidance for scientists and practitioners on the reliability of climate classifications derived from GCMs.

Main Methods:

  • Utilized outputs from 52 Coupled Intercomparison Model Project Phase 6 (CMIP6) models.
  • Generated uncertainty maps for four prominent CCSs: Whittaker-Ricklefs, Holdridge, Thornthwaite-Feddema, and Köppen.
  • Analyzed climate classifications for both present (1980-2014) and future (2015-2100) periods.

Main Results:

  • Developed a comprehensive set of uncertainty maps for four major climate classification systems.
  • These maps highlight areas where GCM-derived classifications are reliable and identify sources of model error.
  • The uncertainty maps complement classification maps, offering crucial insights into model performance.

Conclusions:

  • The generated uncertainty maps are essential tools for interpreting CCSs derived from GCMs.
  • This digital resource aids in minimizing risks and unsubstantiated conclusions in climate change mitigation and adaptation studies.
  • Scientists and practitioners can integrate these maps to enhance the robustness of their climate-related research and planning.