Seasonal precipitation forecast skill over Iran

This paper examines the skill of seasonal precipitation forecasts over Iran using one two-tiered model, three National Multi-Model Ensemble (NMME) models, and two coupled ocean–atmosphere or one-tiered models. These models are, respectively, the ECHAM4.5 atmospheric model that is forced with sea surface temperature (SST) anomalies forecasted using constructed analogue SSTs (ECHAM4.5-SSTCA); the IRI-ECHAM4.5-DirectCoupled, the NASA-GMAO-062012 and the NCEP-CFSv2; and the ECHAM4.5 Modular Ocean Model version 3 (ECHAM4.5-MOM3-DC2) and the ECHAM4.5-GML-NCEP Coupled Forecast System (CFSSST). The precipitation and 850 hPa geopotential height fields of the forecast models are statistically downscaling to the 0.5∘ × 0.5∘ spatial resolution of the Global Precipitation Climatology Centre (GPCC) Version 6 gridded precipitation data, using model output statistics (MOS) developed through the canonical correlation analysis (CCA) option of the Climate Predictability Tool (CPT). Retroactive validations for lead times of up to 3 months are performed using the relative operating characteristic (ROC) and reliability diagrams, which are evaluated for above- and below-normal categories and defined by the upper and lower 75th and 25th percentiles of the data record over the 15-year test period of 1995/1996 to 2009/2010. The forecast models’ skills are also compared with skills obtained by (a) downscaling simulations produced by forcing the ECHAM4.5 with simultaneously observed SST, and (b) the 850 hPa geopotential height NCEP-NCAR (National Centers for Environmental Prediction-National Center for Atmospheric Research) reanalysis data. Downscaling forecasts from most models generally produce the highest skill forecast at lead times of up to 3 months for autumn precipitation – the October-November-December (OND) season. For most seasons, a high skill is obtained from ECHAM4.5-MOM3-DC2 forecasts at a 1-month lead time when the models’ 850 hPa geopotential height fields are used as the predictor fields. For this model and lead time, the Pearson correlation between the area-averaged of the observed and forecasts over the study area for the OND, November-December-January (NDJ), December-January-February (DJF) and January-February-March (JFM) seasons were 0.68, 0.62, 0.42 and 0.43, respectivelyThe Fars Regional Water Organizationhttp://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1097-00882017-09-30hb2017Geography, Geoinformatics and Meteorolog

Multi-model forecast skill for mid-summer rainfall over southern Africa

Southern African December-January-February (DJF) probabilistic rainfall forecast skill is assessed over a 22-year retroactive test period (1980/1981 to 2001/2002) by considering multi-model ensembles consisting of downscaled forecasts from three of the DEMETER models, the ECMWF, Météo-France and UKMO coupled ocean-atmosphere general circulation models. These models are initialized in such a way that DJF forecasts are produced at an approximate 1-month lead time, i.e. forecasts made in early November. Multi-model forecasts are obtained by: i) downscaling each model's 850 hPa geopotential height field forecast using canonical correlation analysis (CCA) and then simply averaging the rainfall forecasts; and ii) by combining the three models' 850 hPa forecasts, and then downscaling them using CCA. Downscaling is performed onto the 0.5° × 0.5° resolution of the CRU rainfall data set south of 10° south over Africa. Forecast verification is performed using the relative operating characteristic (ROC) and the reliability diagram. The performance of the two multi-model combinations approaches are compared with the single-model downscaled forecasts and also with each other. It is shown that the multi-model forecasts outperform the single model forecasts, that the two multi-model schemes produce about equally skilful forecasts, and that the forecasts perform better during El Niño and La Niña seasons than during neutral years.This work was partly sponsored by the Water Research Commission of South Africa (project K5/1492).http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1097-0088hb201

Gathering the evidence and identifying opportunities for future research in climate, heat and health in South Africa : the role of the South African Medical Research Council

Abstract: Background. A changing climate is likely to have widespread and varying impacts on ecosystems and human health. South Africa (SA) is particularly vulnerable to the impacts of climate change, given the projected increases in temperature, and changes in the amount and patterns of rainfall. Moreover, SA’s vulnerability is exacerbated by extreme inequality and poverty. To prepare for the impacts of climate change and to ensure timeous adaptation, a perspective is given on essential heat and health research in the country. Objectives. To gather studies conducted by the South African Medical Research Council (SAMRC)’s Environment and Health Research Unit (EHRU) to illustrate the range of possible research key areas in the climate, heat and health domain and to present future research priorities. Methods. Studies conducted by the SAMRC’s EHRU were gathered and used to illustrate the range of possible research key areas in the climate, heat and health domain. Using national and international published and grey literature, and tapping into institutional research experiences, an overview of research findings to date and future research priorities were developed. Results. Heat and health-related research has focussed on key settings, for example, schools, homes and outdoor work places, and vulnerable groups such as infants and children, the elderly and people with pre-existing diseases. The need to address basic needs and services provision was emphasised as an important priority. Conclusions. High and low temperatures in SA are already associated with mortality annually; these impacts are likely to increase with a changing climate. Critical cross-sectoral research will aid in understanding and preparing for temperature extremes in SA

Prediction of inflows into Lake Kariba using a combination of physical and empirical models

Seasonal climate forecasts are operationally produced at various climate prediction centres around the world. However, these forecasts may not necessarily be objectively integrated into application models in order to help with decision-making processes. The use of hydro- meteorological models may be proven effective for reservoir operations since accurate and reliable prediction of reservoir inflows can provide balanced solution to the problems faced by dam or reservoir managers. This study investigates the use of a combination of physical and empirical models to predict seasonal inflows into Lake Kariba in southern Africa. Two predictions systems are considered. The first uses antecedent seasonal rainfall totals over the upper Zambezi catchment as predictor in a statistical model for estimating seasonal inflows into Lake Kariba. The second and more sophisticated method uses predicted low-level atmospheric circulation of a coupled ocean-atmosphere general circulation model (CGCM) downscaled to the inflows. Forecast verification results are presented for five run-on 3-month seasons; from September to June over an independent hindcast period of 14 years (1995/6 to 2008/9). Verification is conducted using the relative operating characteristic (ROC) and the reliability diagram. In addition to the presented verification statistics, the hindcasts are also evaluated in terms of their economic value as a usefulness indicator of forecast quality for bureaucrats and to the general public. The models in general perform best during the austral mid-summer season of DJF (seasonal onset of inflows) and the autumn season of MAM (main inflow season). Moreover, the prediction system that uses the output of the CGCM is superior to the simple statistical approach. An additional forecast of a recent flooding event (2010/11), which lies outside of the 14-year verification window, is presented to further demonstrate the forecast system’s operational capability during a season of high inflows that caused societal and infrastructure problems over the region.Applied Center for Climate and Earth Systems Science (ACCESS)http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1097-00882017-05-30hb201

ENSO and implications on rainfall characteristics with reference to maize production in the Free State Province of South Africa

El Niño-Southern Oscillation (ENSO) plays an important role in the interannual variability of rainfall in most parts of southern Africa. The effects of ENSO on the rainy season characteristics and possible impacts on rainfed maize production were investigated. The rainy season characteristics of concern are the onset of rains, cessation of rains, duration of rainy season and seasonal rainfall total. 309 climate stations over the Free State Province with rainfall data from 1950 to 2008 were analysed. The rainy season indices were further subdivided into El Niño and La Niña years. The differences in averages of the rainy season indices were determined for the negative phase of ENSO versus the overall averages and for the positive phase of ENSO versus the overall averages. The results of the onset of rains show no clear pattern in the Free State with some areas experiencing late onset and others early onset in both El Niño and La Niña years. However, the cessation of rains occurs early during the El Niño and late in La Niña years over most parts of the province. Consequently, the duration of the rainy season is shorter than normal in El Niño years and longer than normal in La Niña years. Seasonal rainfall is also lower than normal in El Niño years while in La Niña years more cumulative rainfall in mostly observed. As a result, maize production is favoured in La Niña years and reduction in production is normally observed during El Niño years.Agricultural Research Council-Institute for Soil, Climate and Water (Project no.GW57/007)http://www.elsevier.com/locate/pcenf201

Forecasting seasonal rainfall characteristics and onset months over South Africa

Aspects of forecast skill in predicting seasonal characteristics using global climate models (GCMs) are assessed over South Africa. The GCMs output is configured to predict number of rainfall days at South African Weather Service stations exceeding pre-defined threshold values for the austral summer seasons and to predict the rainfall totals of the onset months of the rainy seasons for eight homogeneous rainfall regions of South Africa. Using canonical correlation analysis (CCA) as statistical downscaling technique through model output statistics, the forecast skill levels of coupled ocean–atmosphere and uncoupled atmospheric models are determined through retro-actively generated hindcasts. Both downscaled models have skill in predicting low and high number of rainfall days exceeding pre-defined thresholds for the austral summer seasons as well as rainfall totals of onset months. In addition to the forecast verification results, CCA pattern is performed to determine the dominating atmospheric circulation systems predicted to be controlling rainfall variations for the seasons and months of interest. CCA patterns for both the GCMs indicate that usually when there are anomalously negative (positive) predicted 850 hPa geopotential heights over South Africa, there are anomalously wet (dry) conditions over most parts of South Africa. The work has paved the way for the operational production of seasonal rainfall characteristics over South Africa in real time.The Department of Trade and Industry through Applied Centre for Climate and Earth Systems Science (ACCESS) and the South African Weather Service.http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1097-00882019-04-01hj2018Geography, Geoinformatics and Meteorolog

On the comparison between seasonal predictive skill of global circulation models : coupled versus uncoupled

The study compares one- and two-tiered forecasting systems as represented by the South African Weather Service (SAWS) Coupled Model (SCM) and its atmosphere-only version. In this comparative framework, the main difference between these Global Climate Models (GCMs) resides in the manner in which the sea-surface temperature (SST) is represented. The models are effectively kept similar in all other aspects. This strategy may allow the role of coupling on the predictive skill differences to be better distinguished. The result reveals that the GCMs differ widely in their performances and the issue of superiority of one model over the other is mostly dependent on the ability to a priori determine an optimal global SST field for forcing the Atmospheric General Circulation Model (AGCM). Notwithstanding, the AGCM’s fidelity is reasonably reduced when the AGCM is constrained with persisting SST anomalies to the extent to which the Coupled General Circulation Model (CGCM)’s superiority becomes noticeable. The result suggests that the boundary forcing coming from the optimal SST field plays a significant role in leveraging a reasonable equivalency in the predictive skill of the two GCM configurations.Water Research Commission (WRC) and Applied Centre for Climate & Earth Systems Science (ACCESS).http://agupubs.onlinelibrary.wiley.com/agu/jgr/journal/10.1002/(ISSN)2169-8996/2016-04-30hb2016Geography, Geoinformatics and Meteorolog

The development and prudent application of climate-based forecasts of seasonal malaria in the Limpopo province in South Africa

Seasonal Climate Forecasting (SCF) in South Africa has a history spanning several decades. During this period a number of SCF systems have been developed for the prediction of seasonal-to-interannual variability of rainfall and surface temperatures. Areas of highest predictability, albeit relatively modest, have also been identified. The north-eastern parts of South Africa that includes the Limpopo province has been demonstrated to be one of the areas of highest SCF skill in the country. Statistical post-processing techniques applied to global climate model output were part of this forecast system development, and were subsequently successfully used in the construction of forecasts systems for applications in sectors which are associated with ENSO-driven climate variability, such as dry-land crop yields and river flows. Here we follow a similar post-model processing approach to test SCF systems for application to the incidence of seasonal malaria in Limpopo. The malaria forecast system introduced here makes use of the seasonal rainfall output fields of one of the North American Multi-Model Ensemble (NMME) climate models, which is then linked statistically through multiple linear regression to observed malaria incidence. The verification results as calculated over a 20-year hindcast period show that the season of highest malaria incidence forecast skill is during the austral mid-summer time of December to February. Moreover, the hindcasts based on the NMME model outscore those of statistical forecast models that separately use Indian and Pacific Ocean sea-surface temperatures as predictors, thus justifying the use of physical global climate models for this kind of application. Additional results indicate that model skill levels may include quasi-decadal variability, that the periods over which forecast verification is performed strongly influences forecast skill, and that poorly predicted malaria seasons may have serious financial implications on public health operations.The Japan Agency for Medical Research and Development (AMED; Japan International Cooperation Agency (JICA) through Science and Technology Research Partnership for Sustainable Development (SATREPS) project for iDEWS South Africa.http://www.elsevier.com/locate/envdevhj2021Geography, Geoinformatics and Meteorolog

Dynamical seasonal prediction of southern African summer precipitation

Prediction skill for southern African (16 – 33 E, 22 –35 S) summer precipitation in the Scale Interaction Experiment-Frontier coupled model is assessed for the period of 1982–2008. Using three different observation datasets, deterministic forecasts are evaluated by anomaly correlation coefficients, whereas scores of relative operating characteristic and relative operating level are used to evaluate probabilistic forecasts. We have found that these scores for December–February precipitation forecasts initialized on October 1st are significant at 95 % confidence level. On a local scale, the level of prediction skill in the northwestern and central parts of southern Africa is higher than that in northeastern South Africa. El Nin˜o/Southern Oscillation (ENSO) provides the major source of predictability, but the relationship with ENSO is too strong in the model. The Benguela Nin˜o, the basin mode in the tropical Indian Ocean, the subtropical dipole modes in the South Atlantic and the southern Indian Oceans and ENSO Modoki may provide additional sources of predictability. Within the wet season from October to the following April, the precipitation anomalies in December-February are the most predictable. This study presents promising results for seasonal prediction of precipitation anomaly in the extratropics, where seasonal prediction has been considered a difficult task.Japan Science and Technology Agency (JST) and Japan International Cooperation Agency (JICA) through Science and Technology Research Partnership for Sustainable Development (SATREPS).http://link.springer.com/journal/382hb201