Using climate information for supporting climate change adaptation in water resource management in South Africa

Water resources, and in particular run-off, are significantly affected by climate variability. At present, there are few examples of how the water management sector integrates information about changing intra-annual climate conditions in a systematic manner in developing countries. This paper, using the case study of Cape Town in the Western Cape, South Africa, identifies processes and products to facilitate increased uptake of seasonal climate forecasts among water resource managers. Results suggest that existing seasonal forecasts do not focus enough on specific users’ needs. In order to increase uptake, forecasts need to include information on the likely impact of precipitation variability on runoff and water availability. More opportunities are also needed for those with climate knowledge to interact with water resource managers, particularly in the developing country context where municipal managers’ capacity is strained. Although there are challenges that need to be overcome in using probabilistic climate information, seasonal forecast information tailored to the needs of water resource planners has the potential to support annual planning and is therefore a means of adapting to climate change.     

Climate change impacts and adaptation in cities: a review of the literature

Many of the decisions relating to future urban development require information on climate change risks to cities This review of the academic and “grey” literature provides an overview assessment of the state of the art in the quantification and valuation of climate risks at the city-scale. We find that whilst a small number of cities, mostly in OECD countries, have derived quantitative estimates of the costs of climate change risks under alternative scenarios, this form of analysis is in its infancy. The climate risks most frequently addressed in existing studies are associated with sea-level rise, health and water resources. Other sectors such as energy, transport, and built infrastructure remain less studied. The review has also undertaken a case study to examine the progress in two cities—London and New York—which are relatively advanced in the assessment of climate risks and adaptation. The case studies show that these cities have benefited from stakeholder engagement at an early stage in their risk assessments. They have also benefited from the development of specific institutional responsibilities for co-ordinating such research from the outset. This involvement has been critical in creating momentum and obtaining resources for subsequent in-depth analysis of sectoral impacts and adaptation needs..While low cost climate down-scaling applications would be useful in future research, the greatest priority is to develop responses that can work within the high future uncertainty of future climate change, to build resilience and maintain flexibility. This can best be used within the context of established risk management practices.     

The Use of Seasonal Climate Forecasting in Policymaking: Lessons from Northeast Brazil

This article examines the use of seasonal climate forecasting in public and private efforts to mitigate the impacts of drought in Ceará, Northeast Brazil. Here, forecasts have been directed towards small scale, rainfed agriculturalists as well as state and local level policymakers in the areas of agriculture, water management, and emergency drought relief. In assessing possibilities and constraints of forecast application in Ceará, the present analysis takes into account three types of variables: (a)characteristics of the forecasts; (b) policymaking systems; and (c)institutional environments. We conclude that, on the one hand, several factors in the Ceará case have limited the effectiveness of seasonal climate forecast use. First, the current level of skill of the forecasts is inadequate for the needs of policy development and farmer decisionmaking. Second, forecast information application has been subject to distortion, misinterpretation and political manipulation. Third, focus on the forecast as a product until recently neglected to take into account end users' needs and decisionmaking behavior. On the other hand, climate forecasting has the potential to offer a dramatic opportunity for state and local level bureaucracies to embark on a path of proactive drought planning.     

Identifying climate services needs for national planning: insights from Malawi

The importance of climate services, i.e. providing targeted, tailored, and timely weather and climate information, has gained momentum, but requires improved understanding of user needs. This article identifies the opportunities and barriers to the use of climate services for planning in Malawi, to identify the types of information that can better inform future adaptation decisions in sub-Saharan Africa. From policy analysis, stakeholder interviews, and a national workshop utilizing serious games, it is determined that only 5–10 day and seasonal forecasts are currently being used in government decision making. Impediments to greater integration of climate services include spatial and temporal scale, accessibility, timing, credibility and the mismatch in timeframes between planning cycles (1–5 years) and climate projections (over 20 years). Information that could more usefully inform planning decisions includes rainfall distribution within a season, forecasts with 2–3 week lead times, likely timing and location of extreme events in the short term (1–5 years), and projections (e.g. rainfall and temperature change) in the medium term (6–20 years). Development of a national set of scenarios would also make climate information more accessible to decision makers, and capacity building around such scenarios would enable its improved use in short- to medium-term planning. Improved climate science and its integration with impact models offer exciting opportunities for integrated climate-resilient planning across sub-Saharan Africa. Accrual of positive impacts requires enhanced national capacity to interpret climate information and implement communication strategies across sectors.

Policy relevance

For climate services to achieve their goal of improving adaptation decision making, it is necessary to understand the decision making process and how and when various types of weather and climate information can be incorporated. Through a case study of public sector planning in Malawi, this article highlights relevant planning and policy-making processes. The current use of weather and climate information and needs, over various timescales – sub-annual to short term (1–5 years) to medium term (6–20 years) – is outlined. If climate scientists working with boundary organizations are able to address these issues in a more targeted, sector-facing manner they will improve the uptake of climate services and the likelihood of climate-resilient decisions across sub-Saharan Africa.     

Seasonal Climate Forecasts – Potential Agricultural-Risk Management Tools?

The importance of anchoring seasonal climate forecasts to user needs is examined in this paper. Although it is generally accepted that seasonal climate forecasts have potential value, many constraints preclude the optimal use of these forecasts, including the way forecasts are produced, interpreted and applied in a variety of decision-making processes. In South Africa, a variety of agricultural users exists, ranging from the small-scale farmer to larger commercial farming entities. Useful seasonal are those produced and disseminated with the end user in mind. A retroactive test period during the 1990s, evaluates the perceived impact of incorporating seasonal rainfall forecasts into decisions made by commercial crop farmers in the central parts of South Africa. Although a small sample of commercial farmers was interviewed, the results show some benefits to commercial agriculture if seasonal climate forecast information is continuously and effectively applied over the long-term.     

<Emphasis Type="Italic">Ua ‘afa le Aso</Emphasis> Stormy weather today: traditional ecological knowledge of weather and climate. The Samoa experience

This paper examines traditional ecological knowledge of weather and climate in Samoa, a Polynesian community in the South Pacific. The research found Samoans have their own unique seasonal calendar. The Samoan seasonal calendar is predominantly based on the observations of local environmental changes, which are in turn influenced by weather and climate. Monitoring changes in plants and animal behaviour, for example, are key indicators used by the Samoans to forecast changes in weather and climate. In addition, their communal and family social activities like hunting, fishing and feasting are driven by the seasonal calendar. The Samoans knowledge of cloud formation, conditions conducive to the formation and onset of severe weather systems and seasonal changes in climate, helped them anticipate, plan and adapt to extreme weather and climate events. The ability and knowledge of the Samoans to forecast the onset of extreme weather and climate events, relying predominantly on local environmental changes are vital tools that should be incorporated in the formulation of human induced climate change adaptation strategies.     

An Ensemble Seasonal Forecast of Human Cases of St. Louis Encephalitis in Florida Based on Seasonal Hydrologic Forecasts

We present a method for the ensemble seasonal prediction of human St. Louis encephalitis (SLE) incidence and SLE virus transmission in Florida. We combine empirical relationships between modeled land surface wetness and the incidence of human clinical cases of SLE and modeled land surface wetness and the occurrence of SLE virus transmission throughout south Florida with a previously developed method for generating ensemble, seasonal hydrologic forecasts. Retrospective seasonal forecasts of human SLE incidence are made for Indian River County, Florida, and forecast skill is demonstrated for 2–4 months. A sample seasonal forecast of human SLE incidence is presented. This study establishes the skill of a potential component of an operational SLE forecast system in south Florida, one that provides information well in advance of transmission and may enable early interventions that reduce transmission. Future development of this method and operational application of these forecasts are discussed. The methodology also will be applied to West Nile virus monitoring and forecasting.     

Seasonal climate prediction for South America with FSU Multi-model Synthetic Superensemble algorithm

Summary Objective combination schemes of predictions from different models have been applied to seasonal climate forecasts. These schemes are successful in producing a deterministic forecast superior to individual member models and better than the multi-model ensemble mean forecast. Recently, a variant of the conventional superensemble formulation was created to improve skills for seasonal climate forecasts, the Florida State University (FSU) Synthetic Superensemble. The idea of the synthetic algorithm is to generate a new data set from the predicted multimodel datasets for multiple linear regression. The synthetic data is created from the original dataset by finding a consistent spatial pattern between the observed analysis and the forecast data set. This procedure is a multiple linear regression problem in EOF space. The main contribution this paper is to discuss the feasibility of seasonal prediction based on the synthetic superensemble approach and to demonstrate that the use of this method in coupled models dataset can reduce the errors of seasonal climate forecasts over South America. In this study, a suite of FSU coupled atmospheric oceanic models was used. In evaluation the results from the FSU synthetic superensemble demonstrate greater skill for most of the variables tested here. The forecast produced by the proposed method out performs other conventional forecasts. These results suggest that the methodology and database employed are able to improve seasonal climate prediction over South America when compared to the use of single climate models or from the conventional ensemble averaging. The results show that anomalous conditions simulated over South America are reasonably realistic. The negative (positive) precipitation anomalies for the summer monsoon season of 1997/98 (2001/02) were predicted by Synthetic Superensemble formulation quite well. In summary, the forecast produced by the Synthetic Superensemble approach outperforms the other conventional forecasts.     

Targeting Climate Forecasts for Agricultural Applications in Sub-Saharan Africa: Situating Farmers in User-Space

Several meteorological services in Africa now issue seasonal climate forecasts on an operational basis. However, the failure to develop a comprehensive profile of users has resulted in a considerable gap between the information that is likely to be useful to farmers and that provided and disseminated by these services. The present study develops a methodology to characterize smallholder production systems in order to identify farmer groups who may adopt and benefit from the climate forecast information in sub-Saharan Africa. Through an extensive literature review, data and information was derived from a national household survey of 1540 smallholders in 1995–1997 by the Kenya Agricultural Research Institute and spatial georeferenced data from leading world data centers. The data were analysed and synthesized using the GIS. Considerable opportunities exist for farming communities to improve their profitability using climate forecasts. Although the needs and demand for climate forecasts vary according to the production systems and market forces that determine credit, demand and input availability and, thus, the usability of forecasts depend on the characteristics of the farmers and their place in space. Based on production strategies and options available to farmers, three zones were identified grouping farmers with highly probable, probable and less probable potential of adopting climate forecasts to alter their production practices. Although a climate forecast may be useful to all farmers in the region considered, due to different options available to individual groups of farmers, however, the benefits derived from its use may not be equitable. Some of the options available to farmers in Kenya were considered in this study with a view to highlighting why some may benefit more than others. The methodology demonstrated here could be adopted for other parts of the world for: (1) selecting survey sites to determine the benefits of climate forecasts using farmers participatory rapid rural appraisals and simulation approach, and (2) target climate information where it would be most useful.     

Improvement of seasonal forecasts with inclusion of tropical instability waves on initial conditions

Positive impacts of tropical instability waves (TIWs) in initial conditions on seasonal forecasts are investigated using a air-sea coupled GCM. Due to coarse observational networks and deficiencies in widely-used initialization methods (e.g. 3DVAR or OI methods), TIW variability in oceanic initial conditions is excessively suppressed. It ruins the interaction between TIWs and climate states, therefore, degrades the climate forecast skills. To settle this problem, TIW patterns obtained from free integration is added to the spatially-smoothed initial conditions to simulate realistic seasonal TIW variability (TIWV). Through 20-year ensemble forecast experiments, it is shown that seasonal TIWV with TIWs-seeded initial conditions is significantly stronger until 2-month lead time. In addition, enhanced TIWV amplifies nonlinear relationship between TIWs and ENSO, which leads realistic simulation of the El Ni?o-La Ni?a asymmetry. As a result of better ENSO simulation, correlation improvement of simulated NINO3 index with TIWs-seeded initial conditions is over 0.1 at 4-month lead time.     

Seasonal Forecasts of the Summer 2016 Yangtze River Basin Rainfall

The Yangtze River has been subject to heavy flooding throughout history,and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods.Dams along the river help to manage flood waters,and are important sources of electricity for the region.Being able to forecast high-impact events at long lead times therefore has enormous potential benefit.Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services.The teleconnection from El Ni ?no to Yangtze River basin rainfall meant that the strong El Ni ?no in winter 2015/16 provided a valuable opportunity to test the application of a dynamical forecast system.This paper therefore presents a case study of a real-time seasonal forecast for the Yangtze River basin,building on previous work demonstrating the retrospective skill of such a forecast.A simple forecasting methodology is presented,in which the forecast probabilities are derived from the historical relationship between hindcast and observations.Its performance for2016 is discussed.The heavy rainfall in the May–June–July period was correctly forecast well in advance.August saw anomalously low rainfall,and the forecasts for the June–July–August period correctly showed closer to average levels.The forecasts contributed to the confidence of decision-makers across the Yangtze River basin.Trials of climate services such as this help to promote appropriate use of seasonal forecasts,and highlight areas for future improvements.     

Developing local adaptation strategies for climate change in agriculture: A priority-setting approach with application to Latin America

Even with substantially increased attention to climate adaptation in developing countries in recent years, there are a number of important remaining research needs: better incorporating stakeholder input; using replicable methodologies to provide comparability across different settings; assuring that stakeholder input reflects the results of climate science, not simply perceptions; and effectively linking stakeholder input with the regional and national levels at which policy changes are made. This study reports the results of a methodology for identifying and prioritizing local, stakeholder-driven response options to climate change in agriculture. The approach is based on multi-criteria scoring methods previously applied to research planning and priority-setting in agricultural and natural resource management research, public health, and other areas. The methodology is a sequential approach built around needs assessments by local stakeholders; the incorporation of climate science results; the sharing of these results and climate adaption response options with stakeholders at a series of workshops; stakeholder priority-setting exercises using multi-criteria scoring; and validation with policymakers. The application is to three diverse agroecosystems in Mexico, Peru and Uruguay. Among the many findings is that, notwithstanding the wide diversity of agro-ecosystems, there are numerous similarities in the agricultural adaptation responses prioritized by local stakeholders.     

An architecture for government action on adaptation to climate change. An editorial comment

An architecture of government adaptation programs is presented. Components include leadership, institutional organization, stakeholder involvement, climate change information, appropriate use of decision analysis techniques, explicit consideration of barriers to adaptation, funding for adaptation, technology development and diffusion, and adaptation research. This architecture is a useful heuristic for identifying, evaluating, and reevaluating the needs of decision makers as they improve management of climate-sensitive resources in a changing environment.     

Climate Services for Water Resource Management in China: The Case Study of Danjiangkou Reservoir

The efficiencies and effectiveness of water resource management are inextricably linked to climate services. This study demonstrates a climate information service for Danjiangkou Reservoir, which is the largest artificial lake in Asia, facing mounting challenges for flood control, water storage, and water diversion. Unlike traditional water resource management on the basis of short-term weather forecast and runoff monitoring, subseasonal to seasonal(S2S)and annual climate predictions as well as long-term climate change projections were well used to support the decision makers in Danjiangkou Reservoir. The National Climate Center(NCC) has projected the changes of future climate and extreme events by dynamically downscaling the Coupled Model Intercomparison Project phase 5(CMIP5)projections to 25-km resolution for the long-term planning of water resource management in Danjiangkou Reservoir.Real-time climate predictions based on climate models and downscaling interpretation and application methods at different timescales were also provided to meet the specific needs of earlier predictions and spatial refinement for the short-term diversion of the reservoir. Our results show that such climate services facilitated the Diversion Center of Danjiangkou Reservoir(DCDR) to reasonably control the operational water level, increased the ecological water supply to the northern portion of China by 844 million m~3, and reduced as much as 1.67 billion m~3 of abandoned water in 2019. In the future, it is necessary to develop climate prediction methods to increase spatial and temporal resolutions and prediction skills, and enhance interactions between providers and users.     

Identifying stakeholder-relevant climate change impacts: A case study in the Yakima River Basin,Washington, USA

Designing climate-related research so that study results will be useful to natural resource managers is a unique challenge. While decision makers increasingly recognize the need to consider climate change in their resource management plans, and climate scientists recognize the importance of providing locally-relevant climate data and projections, there often remains a gap between management needs and the information that is available or is being collected. We used decision analysis concepts to bring decision-maker and stakeholder perspectives into the applied research planning process. In 2009 we initiated a series of studies on the impacts of climate change in the Yakima River Basin (YRB) with a four-day stakeholder workshop, bringing together managers, stakeholders, and scientists to develop an integrated conceptual model of climate change and climate change impacts in the YRB. The conceptual model development highlighted areas of uncertainty that limit the understanding of the potential impacts of climate change and decision alternatives by those who will be most directly affected by those changes, and pointed to areas where additional study and engagement of stakeholders would be beneficial. The workshop and resulting conceptual model highlighted the importance of numerous different outcomes to stakeholders in the basin, including social and economic outcomes that go beyond the physical and biological outcomes typically reported in climate impacts studies. Subsequent studies addressed several of those areas of uncertainty, including changes in water temperatures, habitat quality, and bioenergetics of salmonid populations.     

Challenges posed by and approaches to the study of seasonal-to-decadal climate variability

The tasks of providing multi-decadal climate projections and seasonal plus sub-seasonal climate predictions are of significant societal interest and pose major scientific challenges. An outline is presented of the challenges posed by, and the approaches adopted to, tracing the possible evolution of the climate system on these various time-scales. First an overview is provided of the nature of the climate system’s natural internal variations and the uncertainty arising from the complexity and non-linearity of the system. Thereafter consideration is given sequentially to the range of extant approaches adopted to study and derive multi-decadal climate projections, seasonal predictions, and significant sub-seasonal weather phenomena. For each of these three time-scales novel results are presented that indicate the nature (and limitations) of the models used to forecast the evolution, and illustrate the techniques adopted to reduce or cope with the forecast uncertainty. In particular, the contributions (i) appear to exemplify that in simple climate models uncertainties in radiative forcing outweigh uncertainties associated with ocean models, (ii) examine forecast skills for a state-of-the-art seasonal prediction system, and (iii) suggest that long-lived weather phenomena can help shape intra-seasonal climate variability. Finally, it is argued, that co-consideration of all these scales can enhance our understanding of the challenges associated with uncertainties in climate prediction.     

用500hPa环流相似作广东月、季要素预报

本文用500hPa月平均环流场及500hPa合成平均环流场为基本资料，用前期特征月、季的500hPa高度场进行相似定量计算，从中找出最相似的年份个例作出月、季要素预报，该方法在广东短期气候预测业务工作中有一定的参考价值。     

动力-统计客观定量化汛期降水预测研究新进展

汛期降水预测是短期气候预测的重要内容之一,也是难点之一。近20年来,动力-统计相结合的预测方法在解决这一复杂的科学难题方面取得了一定进展。该文系统地介绍了近年来国家级气候预测业务中关于动力-统计客观定量化预测的原理、最优因子订正和异常因子订正两类预测方案,及动力-统计集成的中国季节降水预测系统 (FODAS1.0)。2009—2012年的汛期降水预测中,动力-统计客观定量化预测方法4年平均PS评分为73,距平相关系数为0.16,体现了较高的预报技巧。但该方法仍存在不足,需通过加强气候因子与降水之间关系的诊断分析、完善短期气候模式的物理过程、改进参数化方案及研发有针对性的区域气候模式等手段,进一步提高模式本身的预报技巧,使动力-统计预测方法在汛期降水预测中发挥更大作用。     

季度预测研究回顾

对月平均大气环流预报试验、季度预测和中国汛期降水预测进行了总结。结果表明气候预测的对象必须是要素的时间平均场。利用数值模拟进行气候预测是今后的主要发展方向,而季度预测技巧的提高依赖于对物理参数化和物理机制的研究。最后,讨论了季平均气温和季总降水的可预报性问题,即时效性和准确率。     

Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons

This paper shows demonstrable improvement in the global seasonal climate predictability of boreal summer (at zero lead) and fall (at one season lead) seasonal mean precipitation and surface temperature from a two-tiered seasonal hindcast forced with forecasted SST relative to two other contemporary operational coupled ocean–atmosphere climate models. The results from an extensive set of seasonal hindcasts are analyzed to come to this conclusion. This improvement is attributed to: (1) The multi-model bias corrected SST used to force the atmospheric model. (2) The global atmospheric model which is run at a relatively high resolution of 50 km grid resolution compared to the two other coupled ocean–atmosphere models. (3) The physics of the atmospheric model, especially that related to the convective parameterization scheme. The results of the seasonal hindcast are analyzed for both deterministic and probabilistic skill. The probabilistic skill analysis shows that significant forecast skill can be harvested from these seasonal hindcasts relative to the deterministic skill analysis. The paper concludes that the coupled ocean–atmosphere seasonal hindcasts have reached a reasonable fidelity to exploit their SST anomaly forecasts to force such relatively higher resolution two tier prediction experiments to glean further boreal summer and fall seasonal prediction skill.