Use Of Climate Predictions Research Articles

Subseasonal predictions for climate services, a recipe for operational implementation

The implementation of operational climate service prototypes, which encompasses the co-design and delivery of real-time actionable products with/to stakeholders, contributes to efficiently leveraging operational climate predictions into actionable climate information by providing practical insight on the actual use of climate predictions. This work showcases a general guideline for implementing an operational climate service based on subseasonal predictions. At this timescale, many strategic decisions can benefit from timely predictions of climate variables. Still, the use of subseasonal predictions is not fully exploited. Here, we describe the key aspects considered to set up an operational climate service from the conception to the production phase. These include the choice of the subseasonal systems, the data sources and the methodology employed for post-processing the predictions. To illustrate the process with a real case, we present the detailed workflow design of the implementation of a climate service based on subseasonal predictions and describe the bias adjustment and verification methodologies implemented. This work was developed in the H2020 S2S4E project, where industrial and research partners co-developed a fully-operational Decision Support Tool (DST) providing 18 months of real-time subseasonal and seasonal forecasts tailored to the specific needs of the renewable energy sector. The operational workflow can be adapted to serve forecast products to other sectors, as has been proved in the H2020 vitiGEOSS project, where the workflow was modified to provide downscaled subseasonal predictions to specific locations. We consider this a valuable contribution to future developments of similar service implementations and the producers of the climate data.

Commentary: Communicating climate complexity

Commentary: Communicating climate complexity

Information sharing and climate risk management among Senegalese agropastoralists

Efforts to enhance dissemination and use of climate predictions are often characterized by an implicit treatment of information as a public good. This ignores the ways in which environmental information is embedded in social institutions. For common property resources, important links exist between access to information and control over resource access. This case study of agropastoral livelihoods in Senegal combined data from interviews, household surveys, and focus groups to assess the role of information in the political economy of vulnerability. Fulani agropastoralists in the area rarely used seasonal forecasts in herd management decisions due to delays in availability, the lack of fine-scale data, and the failure to target indicators of interest. Divergent management priorities produced differential utility of climate information for different groups, with commercial producers standing to benefit disproportionately. Achieving vulnerability reduction for subsistence agropastoralists requires attending to the ways in which the production of local knowledge is embedded in social relationships that mediate resource access.

Climate prediction experiences in southern Africa 1990–2005 and key outcomes

This paper reviews long-range climate prediction with a focus on experiences in southern Africa from 1990 to 2005 and statistical techniques based on historical replication. The initial growth and use of climate predictions in southern Africa created an understanding of tropical Atlantic and Indian Ocean coupling with Pacific El Nino and the African monsoon, and fostered extensions of the global observing system as a key outcome. An intercomparison of forecast errors highlights optimal scales for model aggregation. Cases of forecast development are analyzed and a tendency for neutral messages is found. South African maize yield exhibits an uptrend (+0.26 T ha−1/year) since 1992 that may be related to the increased use of Internet and uptake of long-range forecasts.

Improving Farmers’ Perception and Use of Climate Predictions in Farming Decisions: A Transition Model

Abstract Despite tremendous efforts to improve weather and climate predictions and to inform farmers about the use of such weather products, farmers’ attitudes toward forecast use remain poor and farmer use of forecasts has not increased. This paper describes features of a new conceptual model for facilitating farmers’ use of weather products and offers preliminary evidence for its effectiveness based on a test-of-concept prototype. The prototype system provides farmers with contextualized information, the opportunity to use that information in relevant farming contexts, and collaborative interaction with other users. In addition, scaffolding and feedback are incorporated in the model to enhance learning and motivation. Surveys before and after use of the prototype system, and focus-group discussion after system use, were conducted to obtain evaluations from 15 farmers in southeastern Nebraska. Farmers’ evaluations of the system were moderately positive and indicated greater intentions to use the products in the future than they had in the past. However, farmers only slightly increased their positive expectancies of various general categories of weather and climate products, supporting the difficulties associated with changing overall attitudes when attempting to transfer scientific improvements into practical uses. It is suggested that multiple exposures to such a system and more individualized and personally relevant use opportunities may further enhance the power of the proposed model.

Links between Tropical Pacific SST and Cholera Incidence in Bangladesh: Role of the Eastern and Central Tropical Pacific

AbstractRecent studies arising from both statistical analysis and dynamical disease models indicate that there is a link between incidence of cholera, a paradigmatic waterborne bacterial disease (WBD) endemic to Bangladesh, and the El Niño–Southern Oscillation (ENSO). However, a physical mechanism explaining this relationship has not yet been established. A regionally coupled, or “pacemaker,” configuration of the Center for Ocean–Land–Atmosphere Studies atmospheric general circulation model is used to investigate links between sea surface temperature in the central and eastern tropical Pacific and the regional climate of Bangladesh. It is found that enhanced precipitation tends to follow winter El Niño events in both the model and observations, providing a plausible physical mechanism by which ENSO could influence cholera in Bangladesh.The enhanced precipitation in the model arises from a modification of the summer monsoon circulation over India and Bangladesh. Westerly wind anomalies over land to the west of Bangladesh lead to increased convergence in the zonal wind field and hence increased moisture convergence and rainfall. This change in circulation results from the tropics-wide warming in the model following a winter El Niño event. These results suggest that improved forecasting of cholera incidence may be possible through the use of climate predictions.

Links between tropical Pacific SST and cholera incidence in Bangladesh: Role of the eastern and central tropical Pacific

Recent studies arising from both statistical analysis and dynamical disease models indicate that there is a link between incidence of cholera, a paradigmatic waterborne bacterial disease (WBD) endemic to Bangladesh, and the El Niño–Southern Oscillation (ENSO). However, a physical mechanism explaining this relationship has not yet been established. A regionally coupled, or “pacemaker,” configuration of the Center for Ocean–Land–Atmosphere Studies atmospheric general circulation model is used to investigate links between sea surface temperature in the central and eastern tropical Pacific and the regional climate of Bangladesh. It is found that enhanced precipitation tends to follow winter El Niño events in both the model and observations, providing a plausible physical mechanism by which ENSO could influence cholera in Bangladesh. The enhanced precipitation in the model arises from a modification of the summer monsoon circulation over India and Bangladesh. Westerly wind anomalies over land to the west of Bangladesh lead to increased convergence in the zonal wind field and hence increased moisture convergence and rainfall. This change in circulation results from the tropics-wide warming in the model following a winter El Niño event. These results suggest that improved forecasting of cholera incidence may be possible through the use of climate predictions.

Sensitivity of Kelani streamflow in Sri Lanka to ENSO

AbstractAs part of an effort to demonstrate the use of climate predictions for water resources management, the El Niño/Southern Oscillation (ENSO) influences on stream flow in the Kelani River in Sri Lanka were investigated using correlation analysis, composite analysis and contingency tables. El Niño (warm phase of ENSO) was associated with decreased annual stream flow and La Niña (cold phase of ENSO) with increased annual flows. The annual stream flow had a negative correlation with the simultaneous ENSO index of NINO3·4 that was significant at the 95% level. This negative correlation is enhanced to a 99% level if the aggregate January to September or the April to September stream flow alone were considered. Although, there is little correlation between ENSO indices and stream flow during the October to December period, there is a high correlation between rainfall and NINO3·4 (r = 0·51, significant at the 99% level). Therefore ENSO based rainfall predictions can be used along with a hydrological model to predict the October to December stream flow. This study demonstrates the viability of using ENSO based predictors for January to September or April to September stream flow predictions in the Kelani River. The October to December stream flow may be predicted by exploiting the strong relationship between ENSO and rainfall during that period. Copyright © 2003 John Wiley & Sons, Ltd.

El Niño–southern oscillation influences on the Mahaweli streamflow in Sri Lanka

AbstractDespite advances over the last two decades in the capacity to predict the evolution of the El Niño–southern oscillation (ENSO) phenomenon and advances in understanding of the relationship between ENSO and climate, there has been little use of climate predictions for water resources management in the tropics. As part of an effort to develop such a prediction scheme, the ENSO influences on streamflow and rainfall in the upper catchment of the Mahaweli river in Sri Lanka were investigated with correlation analysis, composite analysis and contingency tables. El Niño conditions were often associated with decreased annual flows and La Niña with increased flows. The relationship of streamflow and rainfall with the ENSO index of NINO3 contrasted between January to September and October to December. During El Niño episodes the streamflow declines from January to September, but from October to December there is no clear relationship. On the other hand, rainfall shows a clear increase from October to December and declines during January, February, March, July and August. The simultaneous correlations of NINO3 with the aggregate January to September streamflow (r = −0.50), with January to September rainfall (r = −0.44) and with October to December rainfall (r = 0.48) are all significant at the 99% level. The correlation between one‐season‐in‐advance NINO3 with both January to September streamflow and October to December rainfall remained significant at the 99% level.This study demonstrates the potential of using ENSO‐based predictors for a seasonal hydro‐climatic prediction scheme in the Mahaweli basin. It shows the significant contrasts in ENSO influence on rainfall and streamflow due to various hydrological processes. It has demonstrated that the potential for prediction is improved by investigating ENSO influences for the appropriate season for the given river catchment. Copyright © 2003 Royal Meteorological Society.

El Niño–southern oscillation influences on rice production in Sri Lanka

AbstractDespite advances in the capacity to predict the evolution of the El Niño–southern oscillation (ENSO) phenomenon and advances in understanding the influence of ENSO on rainfall in tropical regions such as Sri Lanka, there has been limited use of climate predictions for agricultural decision‐making. Climatic fluctuations have a profound influence on the cultivation of crops such as rice, which is the staple food in Sri Lanka. Here, the relationship between the sea‐surface temperature‐based ENSO index of NINO3.4, rainfall and the departure of Sri Lankan rice production from long‐term trends, is analysed for the ‘Maha’ (October to March) and ‘Yala’ (April to September) cultivation seasons between 1952 and 1997.During the El Niño phase, the Maha rice production frequently increased (10 out of 15 seasons) and the Yala production frequently decreased (10 out of 14 seasons). Conversely, during the La Niña phase, the Maha production decreased (seven out of ten seasons) and Yala production increased (six out of eight seasons). Floods, state interventions, civil disturbances, fertilizer price hikes and extreme anomalies in the previous season were noted in the majority of seasons in which these ENSO–production linkages were violated.The correlation of the Maha rice production anomaly with the average NINO3.4 from October to December was significant at the 5% level and that with the aggregate October to December rainfall was significant at the 1% level. Yala rice production showed a significant relationship with concurrent NINO3.4 and a strong correlation (r = 0.60) with the previous season's rainfall. Yala cultivation is water constrained, and carryover storage from the previous season is often used to determine the extent of planting.The relationships between ENSO and seasonal rice production and the relationship between Yala rice production and previous Maha rainfall could be used for agricultural management and policy formulation. Copyright © 2002 Royal Meteorological Society.

Advances in application of climate prediction in agriculture

Agricultural ecosystems and their associated business and government systems are diverse and varied. They range from farms, to input supply businesses, to marketing and government policy systems, among others. These systems are dynamic and responsive to fluctuations in climate. Skill in climate prediction offers considerable opportunities to managers via its potential to realise system improvements (i.e. increased food production and profit and/or reduced risks). Realising these opportunities, however, is not straightforward as the forecasting skill is imperfect and approaches to applying the existing skill to management issues have not been developed and tested extensively. While there has been much written about impacts of climate variability, there has been relatively little done in relation to applying knowledge of climate predictions to modify actions ahead of likely impacts. However, a considerable body of effort in various parts of the world is now being focused on this issue of applying climate predictions to improve agricultural systems. In this paper, we outline the basis for climate prediction, with emphasis on the El Niño-Southern Oscillation phenomenon, and catalogue experiences at field, national and global scales in applying climate predictions to agriculture. These diverse experiences are synthesised to derive general lessons about approaches to applying climate prediction in agriculture. The case studies have been selected to represent a diversity of agricultural systems and scales of operation. They also represent the on-going activities of some of the key research and development groups in this field around the world. The case studies include applications at field/farm scale to dryland cropping systems in Australia, Zimbabwe, and Argentina. This spectrum covers resource-rich and resource-poor farming with motivations ranging from profit to food security. At national and global scale we consider possible applications of climate prediction in commodity forecasting (wheat in Australia) and examine implications on global wheat trade and price associated with global consequences of climate prediction. In cataloguing these experiences we note some general lessons. Foremost is the value of an interdisciplinary systems approach in connecting disciplinary knowledge in a manner most suited to decision-makers. This approach often includes scenario analysis based on simulation with credible models as a key aspect of the learning process. Interaction among researchers, analysts and decision-makers is vital in the development of effective applications — all of the players learn. Issues associated with balance between information demand and supply as well as appreciation of awareness limitations of decision-makers, analysts, and scientists are highlighted. It is argued that understanding and communicating decision risks is one of the keys to successful applications of climate prediction. We consider that advances of the future will be made by better connecting agricultural scientists and practitioners with the science of climate prediction. Professions involved in decision making must take a proactive role in the development of climate forecasts if the design and use of climate predictions are to reach their full potential.

Cognitive Illusions, Heuristics, and Climate Prediction

A number of studies in meteorological journals have documented some of the constraints to the effective use of climate forecasts. One major constraint, the considerable difficulty people have in estimating and dealing with probabilities, risk, and uncertainty, has received relatively little attention in the climate field. Some of this difficulty arises from problems known as cognitive illusions or biases. These illusions, and ways to avoid them impacting on decision making, have been studied in the fields of law, medicine, and business. The relevance of some of these illusions to climate prediction is discussed here. The optimal use of climate predictions requires providers of forecasts to understand these difficulties and to make adjustments for them in the way forecasts are prepared and disseminated.

How Agribusiness Uses Climate Predictions: Implications for Climate Research and Provision of Predictions

Abstract The paper presents an analysis of climate prediction needs and uses within six important subsegments of the agribusiness sector. Results are based on a mail survey of 114 managers. Although nearly 70% of the respondents indicated some use of climate predictions in the last year, only 1 in 8 of the respondents used that information in a specific decision. Lack of sufficient accuracy and prediction lead time were identified as two important impediments to current use of climate predictions. Estimates of necessary accuracy levels and lead time are reported both for the group average and by segments of need. Recommendations are offered regarding research needs to enhance climate prediction and activities of the government and the private sector to improve use of climate predictions.