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Statistical Recalibration of GCM Forecasts over Southern Africa Using Model Output Statistics

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  • 1 International Research Institute for Climate Prediction, Lamont-Doherty Earth Observatory of Columbia University, Palisades, New York
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Abstract

A technique for producing regional rainfall forecasts for southern Africa is developed that statistically maps or “recalibrates” large-scale circulation features produced by the ECHAM3.6 general circulation model (GCM) to observed regional rainfall for the December–February (DJF) season. The recalibration technique, model output statistics (MOS), relates archived records of GCM fields to observed DJF rainfall through a set of canonical correlation analysis (CCA) equations. After screening several potential predictor fields, the 850-hPa geopotential height field is selected as the single predictor field in the CCA equations that is subsequently used to produce MOS-recalibrated rainfall patterns. The recalibrated forecasts outscore area-averaged GCM-simulated rainfall anomalies, as well as forecasts produced using a simple linear forecast model. The MOS recalibration is applied to two sets of GCM experiments: for the “simulation” experiment, simultaneous observed sea surface temperature (SST) serves as the lower boundary forcing; for the “hindcast” experiment, the prescribed SSTs are obtained by persisting the previous month's SST anomaly through the forecast period. Pattern analyses performed on the predictor–predictand pairs confirm a robust relationship between the GCM 850-hPa height fields and the rainfall fields. The structure and variability of the large-scale circulation is well characterized by the GCM in both simulation and hindcast mode. Measures of retroactive skill for a 9-yr independent period (1991/92–1999/2000) using the hindcast MOS are obtained for both deterministic and probabilistic forecasts, suggesting that a probabilistic representation of MOS forecasts is potentially more valuable. Finally, MOS is employed to investigate its potential to downscale the GCM large-scale circulation to more specific forecasts of land surface characteristics such as streamflow.

Corresponding author address: Dr. Willem A. Landman, South African Weather Service, Private Bag X097, Pretoria 0001, South Africa. Email: willem@weathersa.co.za

Abstract

A technique for producing regional rainfall forecasts for southern Africa is developed that statistically maps or “recalibrates” large-scale circulation features produced by the ECHAM3.6 general circulation model (GCM) to observed regional rainfall for the December–February (DJF) season. The recalibration technique, model output statistics (MOS), relates archived records of GCM fields to observed DJF rainfall through a set of canonical correlation analysis (CCA) equations. After screening several potential predictor fields, the 850-hPa geopotential height field is selected as the single predictor field in the CCA equations that is subsequently used to produce MOS-recalibrated rainfall patterns. The recalibrated forecasts outscore area-averaged GCM-simulated rainfall anomalies, as well as forecasts produced using a simple linear forecast model. The MOS recalibration is applied to two sets of GCM experiments: for the “simulation” experiment, simultaneous observed sea surface temperature (SST) serves as the lower boundary forcing; for the “hindcast” experiment, the prescribed SSTs are obtained by persisting the previous month's SST anomaly through the forecast period. Pattern analyses performed on the predictor–predictand pairs confirm a robust relationship between the GCM 850-hPa height fields and the rainfall fields. The structure and variability of the large-scale circulation is well characterized by the GCM in both simulation and hindcast mode. Measures of retroactive skill for a 9-yr independent period (1991/92–1999/2000) using the hindcast MOS are obtained for both deterministic and probabilistic forecasts, suggesting that a probabilistic representation of MOS forecasts is potentially more valuable. Finally, MOS is employed to investigate its potential to downscale the GCM large-scale circulation to more specific forecasts of land surface characteristics such as streamflow.

Corresponding author address: Dr. Willem A. Landman, South African Weather Service, Private Bag X097, Pretoria 0001, South Africa. Email: willem@weathersa.co.za

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