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Climate services are playing an increasing role in efforts to build the resilience of African agriculture to a variable and changing climate. Efforts to improve the contribution of climate services to agriculture must contend with substantial differences in national agricultural climate services landscapes. Context-specific factors influence the effectiveness, scalability and sustainability of agricultural climate service, but in ways that are challenging to anticipate. In the context of six countries (Ethiopia, Ghana, Kenya, Mali, Senegal, Zambia), this paper addresses the need to consider differing national contexts when developing strategies to make agricultural climate services in sub-Saharan Africa more effective, scalable and sustainable. Based on authors' collective firsthand knowledge and a review of information from secondary sources, we identify key strengths and weaknesses of climate services relative to agriculture sector needs in the focus countries; and assess factors that have contributed to those differences. Focus countries differ substantially in areas such as the degree of public support, alignment of services with agricultural needs, service delivery channels, degree of decentralization, and public—private-sector balance. These differences have been driven largely by differing national policies, delivery capacity and external actors, but not by responsiveness to agricultural sector demands. Building on the analyses of country differences and their drivers, we then discuss four key opportunities to further strengthen the contribution of climate services to agriculture: (a) leveraging farmer demand to drive scaling and sustainability; (b) exploiting digital innovation within a diverse delivery strategy; (c) balancing public and private sector comparative advantage; and (d) embedding climate services in agricultural extension. For each of these opportunities, we consider how different country contexts can impact the potential effectiveness, scalability and sustainability of services; and how efforts to strengthen those services can account for context-specific drivers to manage the tradeoffs among effectiveness, scalability and sustainability.

While seasonal outlooks have been operational for many years, until recently the extended-range timescale referred to as subseasonal-to-seasonal (S2S) has received little attention. S2S prediction fills the gap between short-range weather prediction and long-range seasonal outlooks. Decisions in a range of sectors are made in this extended-range lead time; therefore, there is a strong demand for this new generation of forecasts. International efforts are under way to identify key sources of predictability, improve forecast skill and operationalize aspects of S2S forecasts; however, challenges remain in advancing this new frontier. If S2S predictions are to be used effectively, it is important that, along with science advances, an effort is made to develop, communicate and apply these forecasts appropriately. In this study, the emerging operational S2S forecasts are presented to the wider weather and climate applications community by undertaking the first comprehensive review of sectoral applications of S2S predictions, including public health, disaster preparedness, water management, energy and agriculture. The value of applications-relevant S2S predictions is explored, and the opportunities and challenges facing their uptake are highlighted. It is shown how social sciences can be integrated with S2S development, from communication to decision-making and valuation of forecasts, to enhance the benefits of ‘climate services’ approaches for extended-range forecasting. While S2S forecasting is at a relatively early stage of development, it is concluded that it presents a significant new window of opportunity that can be explored for application-ready capabilities that could allow many sectors the opportunity to systematically plan on a new time horizon.

Throughout at least the past several centuries, El Niño-Southern Oscillation (ENSO) has played a significant role in human response to climate. Over time, increased attention on ENSO has led to a better understanding of both the physical mechanisms, and the environmental and societal consequences of the phenomenon. The prospects for seasonal climate forecasting emerged from ENSO studies, and were first pursued in ENSO studies. In this paper, we review ENSO's impact on society, specifically with regard to agriculture, water, and health; we also explore the extent to which ENSO-related forecasts are used to inform decision making in these sectors. We find that there are significant differences in the uptake of forecasts across sectors, with the highest use in agriculture, intermediate use in water resources management, and the lowest in health. Forecast use is low in areas where ENSO linkages to climate are weak, but the strength of this linkage alone does not guarantee use. Moreover, the differential use of ENSO forecasts by sector shows the critical role of institutions that work at the boundary between science and society. In a long-term iterative process requiring continual maintenance, these organizations serve to enhance the salience, credibility, and legitimacy of forecasts and related climate services.

The properties of tropical cyclones in three atmospheric general circulation models (AGCMs) with low-resolution are discussed. The models are analysed for a period of 40 years. Characteristics of the tropical cyclones in the models are analysed and compared with those of observations, such as genesis position, number of cyclones, accumulated cyclone activity, number of storm days, tracks, and others. The three AGCMs have different levels of skill in simulating the different aspects of tropical cyclone activity in different regions. Some of the weak and strong features in simulating tropical cyclone activity variables are common for the three models, others are unique for each model and basin. The relation between model tropical cyclones and ENSO is analyzed in a paper currently in preparation.

The main features of three atmospheric general circulation models forced with monthly-mean, observed sea surface temperature for the 1979-1995 period are analyzed and compared with observational data. The ensemble means and intra-ensemble standard deviations of several variables were investigated for the two seasons December-January-February and June-July-August. Correlations of the ensemble mean anomalies with observational data and with the indices Nino-3 and the Atlantic Dipole were also calculated. The probability distribution function of the precipitation at different regions was also examined, as well as the internal and external variance of the precipitation in the three models. No model has uniformly better characteristics than the others. On the contrary, each model has strengths and weaknesses that depend on the region and season.

This review paper presents an assessment of the current state of knowledge and capability in seasonal climate prediction at the end of the 20th century. The discussion covers the full range of issues involved in climate forecasting, including (1) the theory and empirical evidence for predictability; (2) predictions of surface boundary conditions, such as sea surface temperatures (SSTs) that drive the predictable part of the climate; (3) predictions of the climate; and (4) a brief consideration of the application of climate forecasts. Within this context, the research of the coming decades that seeks to address shortcomings in each area is described.