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Predictability of dry season reforecasts over the tropical and the sub-tropical South American region

Authors

  • Adam Frumkin,

    Corresponding author
    1. Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA
    2. Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL 32306, USA
    • A. Frumkin, Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA.
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  • Vasubandhu Misra

    1. Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA
    2. Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL 32306, USA
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Abstract

In this study, we diagnose the fidelity of South American dry season (June–July–August) reforecasts from a global climate model (GCM) and a regional climate model (RCM). This includes a set of downscaled integrations of the RCM that uses a bias correction method called anomaly nesting, which is designed to remove the bias of the GCM that forces the RCM at the lateral boundaries. The models are integrated for seven dry seasons (2001–2007), and each season consists of six ensemble members. For this study, we focus on two primary regions: the Amazon River Basin (ARB) and the subtropical (ST) region.

The paper discusses the regions of model bias for 2 m air temperature and for precipitation within ARB and ST regions first using corresponding independent observations and then with the NCEP Climate Forecast System Reanalysis (CFSR). The paper also dwells on the predictability of the above normal, normal and below normal occurrences of the two variables using signal-to-noise ratios and calculation of the area under the relative operative characteristic (ROC) curve (AUC). The models produced the largest biases of both variables over elevated terrain and within the intertropical convergence zone (ITCZ). Signal-to-noise ratios show that the models exhibit more predictability in ARB than they do in ST and that there is more predictability for surface air temperature than for precipitation. AUCs confirm that temperature is more skilfully predicted than precipitation and that the models exhibit more skill in ARB than in ST. AUCs also show that the anomaly nesting integrations have a limited advantage over the rest with some modest improvements in skill of surface temperature prediction over ARB.

Lastly, we evaluate how the three models depict land-atmosphere interactions during the dry season and compare their results with CFSR. We find conflicting results between the global and regional model predictions and CFSR on the relative coupling strength between the land and the atmosphere during the dry season. Copyright © 2012 Royal Meteorological Society

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