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Climate Change Projections: Characterizing Uncertainty Using Climate Models

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Climate Change Modeling Methodology

Abstract

The atmosphere, ocean, land surfaces, and ice sheets of the Earth are highly complex and coupled systems, with physical laws which describe behavior from the microscopic to the planetary scale.

This chapter was originally published as part of the Encyclopedia of Sustainability Science and Technology edited by Robert A. Meyers. DOI:10.1007/978-1-4419-0851-3

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Abbreviations

Bayes’ Theorem:

A law in probability theory relating the probability of a hypothesis given observed evidence to the often easier to characterize probability of that evidence given the hypothesis. The theorem states that the conditional “posterior” probability of an event A given an event B is equal to the “prior” probability of A multiplied by the likelihood of B given A is true, normalized by the prior probability of B.

Climate sensitivity:

The equilibrium global mean near surface air temperature response in Kelvin to a sustained doubling of the atmospheric carbon dioxide concentration.

CMIP-3:

The Coupled Model Intercomparison Project Phase 3, a set of coordinated model experiments using General Circulation Models from the world’s major modeling centers.

Detection and attribution:

A process whereby spatial “fingerprints” associated with individual climate forcing factors (such as aerosol or greenhouse gas concentrations) are identified and used to quantify whether an observed change exceeds the range of natural internal climate variability (detection) and to attribute it to different causes, that is, different forcings (attribution).

Empirical model:

A model based on fitting empirical data, and thus makes no attempt to justify its representations of the system with any physical basis.

General circulation model (GCM):

A three-dimensional mathematical model for the atmosphere and possibly the ocean, land, and sea ice.

Initial condition ensemble:

A number of simulations using a single climate model, each with a small, unique perturbation to the initial state.

Last glacial maximum (LGM):

A period in the most recent ice age lasting several 1,000 years, peaking approximately 20,000 years ago at the maximum extent of the ice sheets.

Lead time:

The period in between the time at which the forecast is made and the time to be forecasted.

Multi-model ensemble (MME):

A collection of structurally different models from a range of institutions used to perform a coordinated set of experiments.

Parameter space:

The multidimensional domain created by considering the possible values of a number of parameters within a model.

Perturbed physics ensemble:

A set of climate simulations generated by taking a single physical model and altering uncertain parameters within a range of plausibility.

Prior probability (marginal probability):

The probability of an event before any additional data is considered in a Bayesian sense.

Posterior probability:

The probability of an event after considering additional relevant evidence in a Bayesian sense.

Systematic error:

The difference between a model simulation and observations or a poorly represented process which is not reducible by parameter tuning.

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Author information

Authors and Affiliations

  1. National Center for Atmospheric Research, NCAR, 1850 Table Mesa Dr, 80305, Boulder, CO, USA

    Dr. Ben Sanderson

  2. Institute for Atmospheric and Climate Science, ETH, Universitätstrasse 16, Zürich, Switzerland

    Dr. Reto Knutti

Authors
  1. Dr. Ben Sanderson
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  2. Dr. Reto Knutti
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Corresponding author

Correspondence to Ben Sanderson .

Editor information

Editors and Affiliations

  1. Pacific Northwest National Laboratory, Richland, 99352, Washington, USA

    Philip J. Rasch

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Sanderson, B., Knutti, R. (2012). Climate Change Projections: Characterizing Uncertainty Using Climate Models. In: Rasch, P. (eds) Climate Change Modeling Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5767-1_10

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