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Related Concept Videos

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.
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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 't,' or...
Uncertainty: Overview00:59

Uncertainty: Overview

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.
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.
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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 particular...
Confidence Coefficient01:24

Confidence Coefficient

The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under both the...

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Related Experiment Videos

Confidence, uncertainty and decision-support relevance in climate predictions.

D A Stainforth1, M R Allen, E R Tredger

  • 1Tyndall Centre for Climate Change Research, Environmental Change Institute, Centre for the Environment, University of Oxford, South Parks Road, Oxford, UK. das@atm.ox.ac.uk

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|June 16, 2007
PubMed
Summary

Complex climate models are vital for understanding climate change but require reassessment for accurate long-term predictions. New strategies are needed to address model uncertainties and improve communication of climate forecasts.

Related Experiment Videos

Area of Science:

  • Climate Science
  • Environmental Modeling
  • Predictive Analytics

Background:

  • Climate models have advanced significantly over 20 years, becoming crucial for studying climate processes and anthropogenic climate change.
  • There's growing interest in interpreting and analyzing computer model outputs, particularly for natural systems, influencing policy and decision-making.

Purpose of the Study:

  • To reassess the role of complex climate models as predictive tools for decadal and longer timescales.
  • To reconsider strategies for climate model development and experimental design.
  • To categorize and discuss quantification of uncertainty sources in climate modeling.

Main Methods:

  • Categorization of uncertainty sources specific to complex climate models.
  • Discussion of experimental strategies for quantifying model uncertainties.
  • Analysis of calibration challenges for models simulating unprecedented climate states.

Main Results:

  • Complex climate models cannot be meaningfully calibrated for future climate states due to extrapolation challenges.
  • Current generic techniques using observations for calibration or weighting are inappropriate for real-world forecast probabilities.
  • Confidence in climate forecasts is derived from understanding assumptions and sources of uncertainty.

Conclusions:

  • Applying standard calibration techniques to climate models is misleading.
  • Effective communication of assumptions and uncertainties is critical for climate science interaction with society.
  • A reassessment of climate model use and development strategies is necessary for reliable long-term predictions.