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An Assessment of Intraseasonal Variability from 13-Yr GCM Simulations

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  • 1 Climate Systems Analysis Group, Department of Environmental and Geographical Science, University of Cape Town, Rondebosch, South Africa
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Abstract

An assessment of 13-yr simulations of three atmospheric general circulation models (AGCMs) forced by observed sea surface temperatures (SSTs) is presented. The National Centers for Environmental Prediction (NCEP) reanalysis data are used as a baseline for the comparisons. Daily circulation characteristics and interannual variability are investigated in order to improve understanding of the causes of systematic model errors. The focus is to determine the utility of these models in the field of seasonal forecasting.

Daily circulation statistics are well represented by the Hadley Centre Atmospheric Climate Model (HADAM3) but the specific versions of the Center for Ocean–Land–Atmosphere Studies (COLA) and Commonwealth Scientific and Industrial Research Organization (CSIRO9) models examined here produce flow patterns biased toward atmospheric archetype modes characteristic of low spatial variability. All three models show relatively large errors in kinetic energy fields of the vertical mean and shear flow, both in latitudinal placement of the midlatitude jet and geographical location of energy maxima. Evidence suggests that model resolution and model physics affect the accuracy of these simulations.

AGCM interannual variability as forced by sea surface temperatures is realistic in terms of a quasi-SOI (Southern Oscillation index) series and reproduces the El Niño–Southern Oscillation (ENSO) signal above noise levels that are determined from simulations using climatological SSTs. However, rainfall fields over southern Africa show little skill in interannual variability and daily rainfall characteristics indicate that some models are producing too many rain days by up to a factor of 2. Notwithstanding these difficulties, AGCMs, if used carefully, do provide sufficient skillful information for guidance in seasonal forecasting.

Corresponding author address: Dr. Warren Tennant, Climate Systems Analysis Group, Dept. of Environmental and Geographical Science, University of Cape Town, Private Bag, Rondebosch 7701, South Africa. Email: tennant@weathersa.co.za

Abstract

An assessment of 13-yr simulations of three atmospheric general circulation models (AGCMs) forced by observed sea surface temperatures (SSTs) is presented. The National Centers for Environmental Prediction (NCEP) reanalysis data are used as a baseline for the comparisons. Daily circulation characteristics and interannual variability are investigated in order to improve understanding of the causes of systematic model errors. The focus is to determine the utility of these models in the field of seasonal forecasting.

Daily circulation statistics are well represented by the Hadley Centre Atmospheric Climate Model (HADAM3) but the specific versions of the Center for Ocean–Land–Atmosphere Studies (COLA) and Commonwealth Scientific and Industrial Research Organization (CSIRO9) models examined here produce flow patterns biased toward atmospheric archetype modes characteristic of low spatial variability. All three models show relatively large errors in kinetic energy fields of the vertical mean and shear flow, both in latitudinal placement of the midlatitude jet and geographical location of energy maxima. Evidence suggests that model resolution and model physics affect the accuracy of these simulations.

AGCM interannual variability as forced by sea surface temperatures is realistic in terms of a quasi-SOI (Southern Oscillation index) series and reproduces the El Niño–Southern Oscillation (ENSO) signal above noise levels that are determined from simulations using climatological SSTs. However, rainfall fields over southern Africa show little skill in interannual variability and daily rainfall characteristics indicate that some models are producing too many rain days by up to a factor of 2. Notwithstanding these difficulties, AGCMs, if used carefully, do provide sufficient skillful information for guidance in seasonal forecasting.

Corresponding author address: Dr. Warren Tennant, Climate Systems Analysis Group, Dept. of Environmental and Geographical Science, University of Cape Town, Private Bag, Rondebosch 7701, South Africa. Email: tennant@weathersa.co.za

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