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Willem A. Landman and Lisa Goddard

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.

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Willem A. Landman and Simon J. Mason

Abstract

The skill of global-scale sea surface temperature forecasts using a statistically based linear forecasting technique is investigated. Canonical variates are used to make monthly sea surface temperature anomaly forecasts using evolutionary and steady-state features of antecedent sea surface temperatures as predictors. Levels of forecast skill are investigated over several months' lead time by comparing the model performance with a simple forecast strategy involving the persistence of sea surface temperature anomalies. Forecast skill is investigated over an independent test period of 18 yr (1982/83–1999/2000), for which the model training period was updated after every 3 yr. Forecasts for the equatorial Pacific Ocean are a significant improvement over a strategy of random guessing, and outscore forecasts of persisted anomalies beyond lead times of about one season during the development stages of the El Niño–Southern Oscillation phenomenon, but only outscore forecasts of persisted anomalies beyond 6 months' lead time during its most intense phase. Model predictions of the tropical Indian Ocean outscore persistence during the second half of the boreal winter, that is, from about December or January, with maximum skill during the March–May spring season, but poor skill during the autumn months from September to November. Some loss in predictability of the equatorial Pacific and Indian Oceans is evident during the early and mid-1990s, but forecasts appear to have improved in the last few years. The tropical Atlantic Ocean forecast skill has generally been poor. There is little evidence of forecast skill over the midlatitudes in any of the oceans. However, during the spring months significant skill has been found over the Indian Ocean as far south as 20°S and over the southern North Atlantic as far north as 30°N, both of which outscore persistence beyond a lead time of less than about one season.

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Willem A. Landman, Anji Seth, and Suzana J. Camargo

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A regional climate model is tested for several domain configurations over the southwestern Indian Ocean to examine the ability of the model to reproduce observed cyclones and their landfalling tracks. The interaction between large-scale and local terrain forcing of tropical storms approaching and transiting the island landmass of Madagascar makes the southwestern Indian Ocean a unique and interesting study area. In addition, tropical cyclones across the southern Indian Ocean are likely to be significantly affected by the large-scale zonal flow. Therefore, the effects of model domain size and the positioning of its lateral boundaries on the simulation of tropical cyclone–like vortices and their tracks on a seasonal time scale are investigated. Four tropical cyclones, which occurred over the southwestern Indian Ocean in January of the years 1995–97, are studied, and four domains are tested. The regional climate model is driven by atmospheric lateral boundary conditions that are derived from large-scale meteorological analyses. The use of analyzed boundary forcing enables comparison with observed cyclones in these tests. Simulations are performed using a 60-km horizontal resolution and for an extended time integration of about 6 weeks. Results show that the positioning of the eastern boundary of the regional model domain is of major importance in the life cycle of simulated tropical cyclone–like vortices: a vortex entering through the eastern boundary of the regional model is generally well simulated. The size of the domain also has a bearing on the ability of the regional model to simulate vortices in the Mozambique Channel, and the island landmass of Madagascar additionally influences storm tracks. These results show that the regional model can produce cyclonelike vortices and their tracks (with some deficiencies) given analyzed lateral boundary forcing. Statistical analyses of GCM-driven nested model ensemble integrations are now required to further address predictive skill of cyclones in the southwestern Indian Ocean and to test if the model can realistically simulate tropical storm genesis as opposed to advecting existing tropical disturbances entering through the model boundaries.

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Christien J. Engelbrecht, Steven Phakula, Willem A. Landman, and Francois A. Engelbrecht

Abstract

The NCEP CFSv2 and ECMWF hindcasts are used to explore the deterministic subseasonal predictability of the 850-hPa circulation of a large domain over the Atlantic and Indian Oceans that is relevant to the weather and climate of the southern African region. For NCEP CFSv2, 12 years of hindcasts, starting on 1 January 1999 and initialized daily for four ensemble members up to 31 December 2010 are verified against ERA-Interim reanalysis data. For ECMWF, 20 years of hindcasts (1995–2014), initialized once a month for all the months of the year are employed in a parallel analysis to investigate the predictability of the 850-hPa circulation. The ensemble mean for 7-day moving averages is used to assess the prediction skill for all the start dates in each month of the year, with a focus on the start dates in each month that are representative of the week-3 and week-4 hindcasts. The correlation between the anomaly patterns over the study domain shows skill over persistence up into the week-3 hindcasts for some months. The spatial distribution of the correlation between the anomaly patterns show skill over persistence to notably reduce over the domain by week 3. A prominent area where prediction skill survives the longest, occur over central South America and the adjacent Atlantic Ocean.

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J. V. Ratnam, Swadhin K. Behera, Takeshi Doi, Satyban B. Ratna, and Willem A. Landman

Abstract

In an attempt to improve the forecast skill of the austral summer precipitation over South Africa, an ensemble of 1-month-lead seasonal hindcasts generated by the Scale Interaction Experiment–Frontier Research Center for Global Change (SINTEX-F2v) coupled global circulation model is downscaled using the Weather Research and Forecasting (WRF) Model. The WRF Model with two-way interacting domains at horizontal resolutions of 27 and 9 km is used in the study. Evaluation of the deterministic skill score using the anomaly correlation coefficients shows that SINTEX-F2v has significant skill in precipitation forecasts confined to western regions of South Africa. Dynamical downscaling of SINTEX-F2v forecasts using the WRF Model is found to further improve the skill scores over South Africa. However, larger improvements in the skill scores are achieved when the WRF Model is forced by a form of bias-corrected SINTEX-F2v forecasts. The systematic biases in the original fields of the SITNEX-F2v forecasts are removed by superimposing the SINTEX-F2v 6-hourly anomalies over the ERA-Interim 6-hourly climatological fields. The WRF Model forced by the bias-corrected SINTEX-F2v shows significant skill in the forecast anomalies of precipitation over most parts of South Africa. Interestingly, the WRF Model runs with the bias correction did not help to improve the SINTEX-F2v forecast of 2-m air temperatures. Perhaps this is because of the large biases in the precipitation forecast by the WRF Model driven by the bias-corrected SINTEX-F2v. These results are important for potentially improving seasonal forecasts over South Africa.

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Willem A. Landman, David DeWitt, Dong-Eun Lee, Asmerom Beraki, and Daleen Lötter

Abstract

Forecast performance by coupled ocean–atmosphere or one-tiered models predicting seasonal rainfall totals over South Africa is compared with forecasts produced by computationally less demanding two-tiered systems where prescribed sea surface temperature (SST) anomalies are used to force the atmospheric general circulation model. Two coupled models and one two-tiered model are considered here, and they are, respectively, the ECHAM4.5–version 3 of the Modular Ocean Model (MOM3-DC2), the ECHAM4.5-GML–NCEP Coupled Forecast System (CFSSST), and the ECHAM4.5 atmospheric model that is forced with SST anomalies predicted by a statistical model. The 850-hPa geopotential height fields of the three models are statistically downscaled to South African Weather Service district rainfall data by retroactively predicting 3-month seasonal rainfall totals over the 14-yr period from 1995/96 to 2008/09. Retroactive forecasts are produced for lead times of up to 4 months, and probabilistic forecast performance is evaluated for three categories with the outer two categories, respectively, defined by the 25th and 75th percentile values of the climatological record. The resulting forecast skill levels are also compared with skill levels obtained by downscaling forecasts produced by forcing the atmospheric model with simultaneously observed SST in order to produce a reference forecast set. Downscaled forecasts from the coupled systems generally outperform the downscaled forecasts from the two-tiered system, but neither of the two systems outscores the reference forecasts, suggesting that further improvement in operational seasonal rainfall forecast skill for South Africa is still achievable.

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Asmerom F. Beraki, David G. DeWitt, Willem A. Landman, and Cobus Olivier
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Asmerom F. Beraki, David G. DeWitt, Willem A. Landman, and Cobus Olivier

Abstract

The recent increase in availability of high-performance computing (HPC) resources in South Africa allowed the development of an ocean–atmosphere coupled general circulation model (OAGCM). The ECHAM4.5-South African Weather Service (SAWS) Modular Oceanic Model version 3 (MOM3-SA) is the first OAGCM to be developed in Africa for seasonal climate prediction. This model employs an initialization strategy that is different from previous versions of the model that coupled the same atmosphere and ocean models. Evaluation of hindcasts performed with the model revealed that the OAGCM is successful in capturing the development and maturity of El Niño and La Niña episodes up to 8 months ahead. A model intercomparison also indicated that the ECHAM4.5-MOM3-SA has skill levels for the Niño-3.4 region SST comparable with other coupled models administered by international centers. Further analysis of the coupled model revealed that La Niña events are more skillfully discriminated than El Niño events. However, as is typical for OAGCM, the model skill was generally found to decay faster during the spring barrier.

The analysis also showed that the coupled model has useful skill up to several-months lead time when predicting the equatorial Indian Ocean dipole (IOD) during the period spanning between the middle of austral spring and the start of the summer seasons, which reaches its peak in November. The weakness of the model in other seasons was mainly caused by the western segment of the dipole, which eventually contaminates the dipole mode index (DMI). The model is also able to forecast the anomalous upper air circulations, particularly in the equatorial belt, and surface air temperature in the Southern African region as opposed to precipitation.

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J. V. Ratnam, Takeshi Doi, Willem A. Landman, and Swadhin K. Behera

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In this study, we attempted to forecast the onset of summer rains over South Africa using seasonal precipitation forecasts generated by the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2 (SINTEX-F2), seasonal forecasting system. The precipitation forecasts of the 12-member SINTEX-F2 system, initialized on 1 August and covering the period 1998–2015, were used for the study. The SINTEX-F2 forecast precipitation was also downscaled using dynamical and statistical techniques to improve the spatial and temporal representation of the forecasts. The Weather Research and Forecasting (WRF) Model with two cumulus parameterization schemes was used to dynamically downscale the SINTEX-F2 forecasts. The WRF and SINTEX-F2 precipitation forecasts were corrected for biases using a linear scaling method with a 31-day moving window. The results indicate the onset dates derived from the raw and bias-corrected model precipitation forecasts to have realistic spatial distribution over South Africa. However, the forecast onset dates have root-mean-square errors of more than 30 days over most parts of South Africa except over the northeastern province of Limpopo and over the Highveld region of Mpumalanga province, where the root-mean-square errors are about 10–15 days. The WRF Model with Kain–Fritsch cumulus scheme (bias-corrected SINTEX-F2) has better performance in forecasting the onset dates over Limpopo (the Highveld region) compared to other models, thereby indicating the forecast of onset dates over different regions of South Africa to be model dependent. The results of this study are important for improving the forecast of onset dates over South Africa.

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