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Lecture# Climate Models: Basics and Uncertainties

Description

This lecture covers the basics of climate models, starting with the Navier-Stokes equation to describe fluid movements in the atmosphere and oceans. It explains the numerical aspects of solving differential equations, the coupling of global climate model components, and the parametrization of processes. The lecture also delves into the different modules of climate models, such as the atmosphere, ocean, land surface, cryosphere, and regional models. It discusses the uncertainties in climate model projections, including sources like internal variability, model spread, and scenario uncertainties. The complexity of climate models increases to represent various components of the climate system, leading to more advanced Earth system models today.

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Related concepts (53)

HUM-121(b): Global issues: climate B

Le cours présente les enjeux mondiaux liés au climat: système climatique et prévisions ; impacts sur écosystèmes et biodiversité ; cadrage historique et débat public ; objectifs et politiques climatiq

Climate model

Numerical climate models use quantitative methods to simulate the interactions of the important drivers of climate, including atmosphere, oceans, land surface and ice. They are used for a variety of purposes from study of the dynamics of the climate system to projections of future climate. Climate models may also be qualitative (i.e. not numerical) models and also narratives, largely descriptive, of possible futures.

General circulation model

A general circulation model (GCM) is a type of climate model. It employs a mathematical model of the general circulation of a planetary atmosphere or ocean. It uses the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation, latent heat). These equations are the basis for computer programs used to simulate the Earth's atmosphere or oceans. Atmospheric and oceanic GCMs (AGCM and OGCM) are key components along with sea ice and land-surface components.

Ordinary differential equation

In mathematics, an ordinary differential equation (ODE) is a differential equation (DE) dependent on only a single independent variable. As with other DE, its unknown(s) consists of one (or more) function(s) and involves the derivatives of those functions. The term "ordinary" is used in contrast with partial differential equations which may be with respect to one independent variable. A linear differential equation is a differential equation that is defined by a linear polynomial in the unknown function and its derivatives, that is an equation of the form where a_0(x), .

Differential equation

In mathematics, a differential equation is an equation that relates one or more unknown functions and their derivatives. In applications, the functions generally represent physical quantities, the derivatives represent their rates of change, and the differential equation defines a relationship between the two. Such relations are common; therefore, differential equations play a prominent role in many disciplines including engineering, physics, economics, and biology.

Numerical analysis

Numerical analysis is the study of algorithms that use numerical approximation (as opposed to symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics). It is the study of numerical methods that attempt at finding approximate solutions of problems rather than the exact ones. Numerical analysis finds application in all fields of engineering and the physical sciences, and in the 21st century also the life and social sciences, medicine, business and even the arts.