Modelling climate and the nature of climate models: A review

Shine, Keith P.; Henderson‐Sellers, A.

doi:10.1002/joc.3370030107

Cited by 17 publications

(4 citation statements)

References 73 publications

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“…A brief introduction to general circulation models An introduction to the general circulation models (GCMs) used in climate studies can be found in Shine and Henderson-Sellers (1983). A more technical but well-explained account of atmospheric dynamics and the basic equations used in numerical climate and weather forecasting models can be found in Atkinson (19811, a book designed for the general scientific reader.…”

Section: Using a General Circulation Model O F The Atmosphere To Simumentioning

confidence: 99%

Prediction of seasonal rainfall in the sahel region using empirical and dynamical methods

Folland

Owen

Ward

et al. 1991

Journal of Forecasting

253

168

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Long-range weather forecasting is a notoriously difficult area of environmental science. However, recent improved understanding of atmospheric dynamics and better observations indicate that useful progress, rooted in scientifically sound ideas, may be possible with longrange forecasting in the tropics. We describe recent research into the mechanisms and prediction of rainfall in the sub-Sahara during the main summer rainfall season, concentrating on the Sahel region. We use a complex physical model of the atmosphere (a 'general circulation' model) and two relatively simple statistical models to show that large-scale variations in sea surface temperature (SST) can strongly influence seasonal Sahel rainfall. Persistence of patterns of SST anomalies (deviations from long-term average) is sufficient to allow useful forecasting techniques to be based on fields of SST anomalies observed in the preceding spring. However, persistence of the SST anomalies may not always be sufficient to provide a skilful forecast.

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Section: Using a General Circulation Model O F The Atmosphere To Simumentioning

confidence: 99%

Prediction of seasonal rainfall in the sahel region using empirical and dynamical methods

Folland

Owen

Ward

et al. 1991

Journal of Forecasting

253

168

View full text Add to dashboard Cite

show abstract

“…One such laudable undertaking, among many others, was that of the US NAS which was recently published in a voluminous report (CDAC, 1983). Some of the major uncertainties which affect the reliability of output from GCMs include physical processes and boundary conditions, regional climate changes and transient changes due to the combined greenhouse gases (Liss and Crane,1983 ;Manabe, 1983;Shine and Henderson-Sellers, 1983;Rind and Lebedeff, 1984).…”

Section: Discussion and Outlookmentioning

confidence: 99%

CO 2-sensitivity experiments using general circulation models

Bach¹

1984

Progress in Physical Geography: Earth and Environment

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According to Lorenz (1975) we can distinguish two types of climate prediction: predictability of the first kind, i.e. the long-range evolution of climate under constant boundary conditions in dependence on the initial climatic state, and predictability of the second kind, i.e. the assessment of the equilibrium response of the climate system to a prescribed change in external influences. With the first type climatic factors can be predicted for millions of years which would be of interest in the study e.g. of ice ages. With the second kind the response of the the observed temperature and precipitation data plus the model-generated data, and

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“…The recognition that different aspects of the climate system demand different types of simplification has led to the development of a wide variety of climate model types (e.g. Shine and Henderson-Sellers, 1983;Henderson-Sellers and McGuffie, 1999). In selecting the type of climate model to be employed for a particular simulation task, it is necessary to balance socio-economic and scientific understanding and outcome demands against available computational capability, and review all three in the context of the data with which parameterizations and initializations will be established (cf.…”

Section: Complexity 6ersus Conceptualizationmentioning

confidence: 99%

Forty years of numerical climate modelling

McGuffie

Henderson‐Sellers

2001

Intl Journal of Climatology

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Climate modelling is now a mature discipline approaching its fortieth birthday. The need for valid climate forecasts has been underlined by the recognition that human activities are now modifying the climate. The complex nature of the climate system has resulted in the development of a surprisingly large array of modelling tools. Some are relatively simple, such as the earth systems and energy balance models (EBMs), while others are highly sophisticated models which challenge the fastest speeds of the most powerful supercomputers. Indeed, this discipline of the latter half of the twentieth century is so critically dependent on the availability of a means of undertaking powerful calculations that its evolution has matched that of the digital computer. The multi-faceted nature of the climate system demands high quality, and global observations and innovative parameterizations through which processes which cannot be described or calculated explicitly are captured to the extent deemed necessary. Interestingly, results from extremely simple, as well as highly complex and many intermediate model types are drawn upon today for effective formulation and evaluation of climate policies. This paper discusses some of the important developments during the first 40 years of climate modelling from the first models of the global atmosphere to today's models, which typically consist of integrated multi-component representations of the full climate system. The pressures of policy-relevant questions more clearly underline the tension between the need for evaluation against quality data and the unending pressure to improve spatial and temporal resolutions of climate models than at any time since the inception of climate modelling.

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Modelling climate and the nature of climate models: A review

Cited by 17 publications

References 73 publications

Prediction of seasonal rainfall in the sahel region using empirical and dynamical methods

Prediction of seasonal rainfall in the sahel region using empirical and dynamical methods

CO 2-sensitivity experiments using general circulation models

Forty years of numerical climate modelling

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