Abstract

Remote effects due to the tropical disturbances in the north Indian Ocean are investigated by analyzing long-lasting (≥5 days) tropical disturbances, which reached at least the strength of tropical storms. The present analysis is carried out for both the pre- and postmonsoon periods. The spatial and temporal distribution of the outgoing longwave radiation (OLR) during the premonsoon disturbances over the Bay of Bengal reveals several interesting features. Temporal distribution of the OLR anomalies shows that the intraseasonal oscillations play an important role in the formation of those disturbances. The spatial distribution of the OLR anomalies shows a dipole with negative OLR anomalies over the bay and positive OLR anomalies over the Indonesian region. The atmospheric response to the negative OLR anomalies results in positive temperature anomalies over northwest India, Pakistan, Afghanistan, Iran, and Saudi Arabia, remote from the disturbance; and the response to the positive anomalies causes slight increase in the sea surface temperature of the Arabian Sea. Negative OLR anomalies are also seen over western Japan due to the Rossby waves generated by the heating over the Bay of Bengal besides the enhancement of the so-called “Pacific–Japan” teleconnection pattern. However, the analysis shows that the postmonsoon disturbances over the Bay of Bengal and the disturbances formed over the Arabian Sea in both pre- and postmonsoon seasons do not develop remote teleconnections associated with the above type of Rossby wave mechanism. These results are significant for the short- to medium-range weather forecast over a wide range covering Japan, Pakistan, Afghanistan, Iran, and Saudi Arabia.

Abstract

Periods of low convective activity over southern Africa during the peak rainy season from December to February are known to be due to the northeastward displacement of the tropical temperate trough (TTT) systems from the landmass. In this study, using Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim) data, the authors show that the displacement of the TTT systems during long periods of low convective activity has origins in the Northern Hemisphere. Using standardized area-averaged outgoing longwave radiation (OLR) daily anomalies over southern Africa, long periods of low convective activity are defined as periods of positive OLR anomalies lasting consecutively for 5 or more days with a standard deviation of 1 or more. An eddy streamfunction anomaly composite of the periods of low convective activity shows an upper-level anomalous wave originating in the Northern Hemisphere and extending to southern Africa from the eastern Pacific and displacing the tropical–extratropical cloud bands from the southern African landmass into the southwestern Indian Ocean. The wave train is also seen to generate an anticyclonic anomaly over southern Africa, resulting in suppressed convective activity. Understanding the causes of the long periods of low convective activity will help in improving their predictability and also the predictability of seasonal rainfall over southern Africa.

Abstract

Distinct pattern of interannual variability in sea surface temperature (SST) in the South Pacific [i.e., the South Pacific subtropical dipole (SPSD)] is examined using outputs from a coupled general circulation model. The SPSD appears as the second empirical orthogonal function (EOF) mode of the SST anomalies in the South Pacific and is associated with a northeast–southwest-oriented dipole of positive and negative SST anomalies in the central basin. The positive and negative SST anomaly poles start to develop during austral spring, reach their peak during austral summer, and gradually decay afterward. Close examination of mixed-layer heat balance yields that the SST anomaly poles develop mainly because warming of the mixed layer by shortwave radiation is modulated by the anomalous mixed-layer thickness. Over the positive (negative) pole, the mixed layer becomes thinner (thicker) than normal and acts to enhance (reduce) the warming of the mixed layer by climatological shortwave radiation. This thinner (thicker) mixed layer may be related to the suppressed (enhanced) evaporation associated with the overlying sea level pressure (SLP) anomalies. Weaker-than-normal surface wind also contributes to the thinner mixed layer in the case of the positive pole. Furthermore, the SLP anomalies are linked with the geopotential height anomalies in the upper troposphere and are associated with a stationary Rossby wave pattern along the westerly jet in the midlatitudes. This suggests that the SLP anomalies that generate the SPSD are not locally excited but remotely induced signals.

Abstract

Remote effects modulating the austral summer precipitation over southern Africa during El Niño/El Niño Modoki events are investigated by analyzing the observed events during December–February of the years from 1982/83 to 2010/11. Based on the composite analyses, it is found that southern Africa experiences significantly below normal precipitation during El Niño events compared to El Niño Modoki events. During these latter events, precipitation anomalies are not so significant although southern Africa as a whole receives below normal precipitations. The differences in the spatial distribution of precipitation over southern Africa are seen to be related to the sea surface temperature (SST) anomalies of the equatorial Pacific through atmospheric teleconnections.

The low-level (850 hPa) Matsuno–Gill response to anomalously high precipitation over the Pacific during El Niño events results in an anomalous anticyclone extending from the equatorial to the subtropical South Indian Ocean. These anomalous anticyclonic winds weaken the tropical moisture flow into the southern Africa landmass. Rossby wave activity flux analysis of the upper-level (300 hPa) circulation shows an anomalous tropospheric stationary wave from the Pacific propagating toward southern Africa and maintaining an anomalous anticyclone over southern Africa. The anomalous Matsuno–Gill response and the anomalous tropospheric stationary wave response are intense during El Niño events, causing drought over southern Africa. During El Niño Modoki events, these processes are weaker compared to El Niño events.

Abstract

An ensemble of 1-month-lead seasonal retrospective forecasts generated by the Scale Interaction Experiment (SINTEX)–Frontier Research Center for Global Change (FRCGC), version 2 tuned for performance on a vector supercomputer (SINTEX-F2v), coupled global circulation model (CGCM) were downscaled using the Weather Research and Forecasting (WRF) Model to improve the forecast of the austral summer precipitation and 2-m air temperatures over Australia. A set of four experiments was carried out with the WRF Model to improve the forecasts. The first was to drive the WRF Model with the SINTEX-F2v output, and the second was to bias correct the mean component of the SINTEX-F2v forecast and drive the WRF Model with the corrected fields. The other experiments were to use the SINTEX-F2v forecasts and the mean bias-corrected SINTEX-F2v forecasts to drive the WRF Model coupled to a simple mixed layer ocean model. Evaluation of the forecasts revealed the WRF Model driven by bias-corrected SINTEX-F2v forecasts to have a better spatial and temporal representation of forecast precipitation and 2-m air temperature, compared to SINTEX-F2v forecasts. Using a regional coupled model with the bias-corrected SINTEX-F2v forecast as the driver further improved the skill of the precipitation forecasts. The improvement in the WRF Model forecasts is due to better representation of the variables in the bias-corrected SINTEX-F2v forecasts driving the WRF Model. The study brings out the importance of including air–sea interactions and correcting the global forecasts for systematic biases before downscaling them for societal applications over Australia. These results are important for potentially improving austral summer seasonal forecasts over Australia.

Abstract

The machine learning technique, namely artificial neural networks (ANN), is used to predict the surface air temperature (SAT) anomalies over Japan in the winter months of December, January, and February for the period 1949/50–2019/20. The predictions are made for the four regions Hokkaido, North, Central, and West of Japan. The inputs to the ANN model are derived from the anomaly correlation coefficients among the SAT anomalies over the regions of Japan and the global SAT and sea surface temperature anomalies. The results are validated using anomaly correlation coefficient (ACC) skill scores with the observation. It is found that the ANN predictions over Hokkaido have higher ACC skill scores compared to the ACC scores over the other three regions. The ANN-predicted SAT anomalies are compared with that of ensemble mean of eight of the North American Multimodel Ensemble (NMME) models besides comparing them with the persistent anomalies. The ANN predictions over all the four regions have higher ACC skill scores compared to the NMME model skill scores in the common period of 1982/83–2018/19. The ANN-predicted SAT anomalies also have higher hit rate and lower false alarm rate compared to the NMME-predicted SAT anomalies. All these indicate that the ANN model is a promising tool for predicting the winter SAT anomalies over Japan.

Abstract

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.

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.

Abstract

The selective ensemble mean (SEM) technique is applied to the late spring and summer months (May–August) surface air temperature anomaly predictions of the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2 (SINTEX-F2), coupled general circulation model over Japan. Using the Köppen–Geiger climatic classification we chose four regions over Japan for applying the SEM technique. The SINTEX-F2 ensemble members for the SEM are chosen based on the anomaly correlation coefficients (ACC) of the SINTEX-F2 predicted and observed surface air temperature anomalies. The SEM technique is applied to generate the forecasts of the surface air temperature anomalies for the period 1983–2018 using the selected members. Analysis shows the ACC skill score of the SEM prediction to be higher compared to the ACC skill score of predictions obtained by averaging all the 24 members of the SINTEX-F2 (ENSMEAN). The SEM predicted surface air temperature anomalies also have higher hit rate and lower false alarm rate compared to the ENSMEAN predicted anomalies over a range of temperature anomalies. The results indicate the SEM technique to be a simple and easy to apply method to improve the SINTEX-F2 predictions of surface air temperature anomalies over Japan. The better performance of the SEM in generating the surface air temperature anomalies can be partly attributed to realistic prediction of 850-hPa geopotential height anomalies over Japan.

Abstract

The prediction skill of dynamical downscaling is evaluated for climate forecasts over southern Africa using the Advanced Research Weather Research and Forecasting (WRF) model. As a case study, forecasts for the December–February (DJF) season of 2011/12 are evaluated. Initial and boundary conditions for the WRF model were taken from the seasonal forecasts of the Scale Interaction Experiment-Frontier Research Center for Global Change (SINTEX-F) coupled general circulation model. In addition to sea surface temperature (SST) forecasts generated by nine-member ensemble forecasts of SINTEX-F, the WRF was also configured to use SST generated by a simple mixed layer Price–Weller–Pinkel ocean model coupled to the WRF model. Analysis of the ensemble mean shows that the uncoupled WRF model significantly increases the biases (errors) in precipitation forecasted by SINTEX-F. When coupled to a simple mixed layer ocean model, the WRF model improves the spatial distribution of precipitation over southern Africa through a better representation of the moisture fluxes. Precipitation anomalies forecasted by the coupled WRF are seen to be significantly correlated with the observed precipitation anomalies over South Africa, Zimbabwe, southern Madagascar, and parts of Zambia and Angola. This is in contrast to the SINTEX-F global model precipitation anomaly forecasts that are closer to observations only for parts of Zimbabwe and South Africa. Therefore, the dynamical downscaling with the coupled WRF adds value to the SINTEX-F precipitation forecasts over southern Africa. However, the WRF model yields positive biases (>2°C) in surface air temperature forecasts over the southern African landmass in both the coupled and uncoupled configurations because of biases in the net heat fluxes.