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There is growing awareness among governments, businesses, and the general public of risks arising from changes to our climate on time scales from months through to decades. Some climatic changes could be unprecedented in their harmful socioeconomic impacts, while others with adequate forewarning and planning could offer benefits. There is therefore a pressing need for decision-makers, including policy-makers, to have access to and to use high-quality, accessible, relevant, and credible climate information about the past, present, and future to help make better-informed decisions and policies. We refer to the provision and use of such information as climate services. Established programs of research and operational activities are improving observations and climate monitoring, our understanding of climate processes, climate variability and change, and predictions and projections of the future climate. Delivering climate information (including data and knowledge) in a way that is usable and useful for decision-makers has had less attention, and society has yet to optimally benefit from the available information. While weather services routinely help weather-sensitive decision-making, similar services for decisions on longer time scales are less well established. Many organizations are now actively developing climate services, and a growing number of decision-makers are keen to benefit from such services. This article describes progress made over the past decade developing, delivering, and using climate services, in particular from the worldwide effort galvanizing around the Global Framework for Climate Services under the coordination of UN agencies. The article highlights challenges in making further progress and proposes potential new directions to address such challenges.

Producing probabilistic subseasonal forecasts of extreme events up to six weeks in advance is crucial for many economic sectors. In agribusiness, this time scale is particularly critical because it allows for mitigation strategies to be adopted for counteracting weather hazards and taking advantage of opportunities. For example, spring frosts are detrimental for many nut trees, resulting in dramatic losses at harvest time. To explore subseasonal forecast quality in boreal spring, identified as one of the most sensitive times of the year by agribusiness end users, we build a multisystem ensemble using four models involved in the Subseasonal to Seasonal Prediction project (S2S). Two-meter temperature forecasts are used to analyze cold spell predictions in the coastal Black Sea region, an area that is a global leader in the production of hazelnuts. When analyzed at the global scale, the multisystem ensemble probabilistic forecasts for near-surface temperature are better than climatological values for several regions, especially the tropics, even many weeks in advance; however, in the coastal Black Sea, skill is low after the second forecast week. When cold spells are predicted instead of near-surface temperatures, skill improves for the region, and the forecasts prove to contain potentially useful information to stakeholders willing to put mitigation plans into effect. Using a cost–loss model approach for the first time in this context, we show that there is added value of having such a forecast system instead of a business-as-usual strategy, not only for predictions released 1–2 weeks ahead of the extreme event, but also at longer lead times.

Little has been documented about the benefits and impacts of the recent growth in climate services, despite a growing call to justify their value and stimulate investment. Regional Climate Outlook Forums (RCOFs), an integral part of the public and private enterprise of climate services, have been implemented over the last 20 years with the objectives of producing and disseminating seasonal climate forecasts to inform improved climate risk management and adaptation. In proposing guidance on how to measure the success of RCOFs, we offer three broad evaluative categories that are based on the primary stated goals of the RCOFs: 1) quality of the climate information used and developed at RCOFs; 2) legitimacy of RCOF processes focused on consensus forecasts, broad user engagement, and capacity building; and 3) usability of the climate information produced at RCOFs. Evaluating the quality of information relies largely on quantitative measures and statistical techniques that are standardized and transferrable, but assessing the RCOF processes and perceived usability of RCOF products will necessitate a combination of quantitative and qualitative social science methods that are sensitive to highly variable regional contexts. As RCOFs have taken up different formats and procedures to adapt to diverse institutional and political settings and varied technical and scientific capacities, objective evaluation methods adopted should align with the goals and intent of the evaluation and be performed in a participatory, coproduction manner where producers and users of climate services together design the evaluation metrics and processes. To fully capture the potential benefits of the RCOFs, it may be necessary to adjust or recalibrate the goals of these forums to better fit the evolving landscape of climate services development, needs, and provision.

East Africa experiences chronic food insecurity, with levels varying from year-to-year across the region. Given that much can be done to prevent this level of suffering before it happens, humanitarian agencies monitor early indicators of food insecurity to trigger early action. Forecasts of total seasonal rainfall are one tool used to monitor and anticipate food security outcomes. Factors beyond rainfall, such as conflict, are key determinants of whether lack of rainfall can become a problem. In this paper, we present a quantitative analysis that isolates the value of rainfall information in anticipating food security outcomes across livelihood groups in East Africa. Comparing observed rainfall and temperature with food security classifications, we quantify how much the chance of food insecurity increases when rainfall is low. Results differed dramatically among livelihood groups; pastoralists in East Africa more frequently experience food insecurity than do non-pastoralists, and 12 months of low rainfall greatly increases the chances of "crisis" and "emergency" food security in pastoralist regions. In non-pastoralist regions, the relationship with total rainfall is not as strong. Similar results were obtained for livelihood groups in Kenya and Ethiopia, with slightly differing results in Somalia. Given this, we evaluated the relevance of monitoring and forecasting seasonal total rainfall. Our quantitative results demonstrate that six months of rainfall observations can already indicate a heightened risk of food insecurity, a full six months before conditions deteriorate. Combining rainfall observations with seasonal forecasts can further change the range of possible outcomes to indicate higher or lower risk of food insecurity, but the added value of seasonal forecasts is noticeable only when they show a strong probability of below-normal rainfall.

In this chapter, we examine how weather forecasts are made and how they may be used by the health sector. We explore why, where and when we might obtain good forecasts and why sometimes the forecasts go wrong. We consider the theoretical and practical constraints on weather forecasts and why their accuracy declines rapidly after only a few days. Knowing the limitations of weather forecasts helps us to learn how to make best use of such information.

Throughout this book, we have sought to identify how, when and where climate information, based on historical data, monitoring products and predictions of future weather and climate can be used to inform health policy and practice. We have prioritized operational information over research opportunities to ensure a focus on practical outcomes. In this chapter, we provide a short review of climate information currently available, as described in detail in Chapters 4-9. We then explore how developments in technologies, institutional arrangements and the education of health professionals are providing new opportunities for translating climate information into a new resource for health-sector decision-making.

In this chapter we describe how climate variables are measured (e.g., ground observation, remote sensing or modelled data), how the data are collected and how they are shared. We discuss the advantages and disadvantages of different data sources and highlight the potential advantages of climate data made available through National Meteorological and Hydrological Services. The use of observed climate monitoring products in the development of early warning systems is highlighted

This study explores the predictive skill of seasonal rainfall characteristics for the first rainy (and planting) season, May–June, in Central America. Statistical predictive models were built using a Model Output Statistics (MOS) technique based on canonical correlation analysis, in which variables that forecast with the Climate Forecast System version 2 (CFSv2) were used as candidate predictors for the observed total precipitation, frequency of rainy days and mean number of extremely dry and wet events in the season. CFSv2 initializations from February to April were explored. The CFSv2 variables used in the study consist of rainfall, as in a typical MOS technique, and a combination of low-level winds and convective available potential energy (CAPE), a blend that has been previously shown to be a good predictor for convective activity. The highest predictive skill was found for the seasonal frequency of rainy days, followed by the mean frequency of dry events. In terms of candidate predictors, the zonal transport of CAPE (uCAPE) at 925 hPa offers higher skill across Central America than rainfall, which is attributed in part to the high model uncertainties associated with precipitation in the region. As expected, dynamical model predictors initialized in February provide lower skill than those initialized later. Nonetheless, the skill is comparable for March and April initializations. These results suggest that the National Meteorological and Hydrological Services in Central America, and the Central American Regional Climate Outlook Forum, can produce earlier more skilful forecasts for May–June rainfall characteristics than previously stated.

In this chapter, we explore the potential value of seasonal climate forecasts to the health community. We begin by considering why the general weather conditions over a season might be predictable, before examining how seasonal forecasts are made, and why they are presented in a different way to weather forecasts. We then examine where and when seasonal forecasts work best, and emphasize that it is possible to make useful seasonal forecasts for health outcomes only in some parts of the world and for certain times of the year (and possibly only for some years). We also review some of the main sources of seasonal forecasts.

In writing this book, we have prioritized shorter timescales of weather and climate variability because these have most direct relevance to the types of operational and planning decisions made in the health sector. However, in this chapter, we focus on longer-term health decisions that require climate knowledge and information at timescales beyond a year to multiple decades (up to 50 years). This time span incorporates both natural decadal variability and the influences of anthropogenic climate change. It is an extremely challenging timeframe at which to work because of the very limited operational predictive skill at multiannual timescales, and limited confidence about how rainfall may change on longer timescales. Despite these challenges, the timescales under consideration, especially the next five to 20 years, are particularly important when considering how to adapt to climate change. Here we discuss what we do and do not know about the climate in the coming years. We then focus on the practical use of climate information in understanding observed climate impacts on health as well as the prediction of multi-annual to multi-decadal risks. The multi-annual timescale is often referred to as ‘decadal’, even though the forecasts are for periods less than a decade.

In this chapter we consider a range of factors that need to be taken into account when seeking to use climate information to improve health decision-making. Identifying causal mechanisms that link climate drivers with specific health issues is an important starting point for policy-makers. Matching decision time-horizons to climate information in a way that takes account of scale issues, uncertainties in the underlying data and modelling approaches as well as institutional barriers to knowledge and data sharing is also critical. And of course, all of this is dependent on a solid understanding of the climate information (including its limitations) that is available to health decision-makers. A researcher may be satisfied with a simple times-series of climate data from an authoritative source; a decision-maker needs to know that the climate information is robust, available for routine use and scalable (i.e., can be used over the entire region of interest).

In 2014, Jamaica entered one of the worst droughts in past 40 years. The drought was associated with the El Niño of 2014-15. Losses to agriculture and to farmers’ livelihoods were substantial, but not all farmers suffered equally. This chapter describes a seasonal drought forecast service that was developed by the Jamaican Meteorological Service (JMS) and the Rural and Agricultural Development Agency (RADA) to help farmers anticipate and prepare for drought. The service, which integrated new technical scientific information, interactive farmers’ forums, and various ways of communicating the information, grew directly out of Jamaica’s stakeholder-driven climate policy process. Jamaica’s success offers a useful example for how high-level planning, such as National Adaptation Plan processes and Nationally Determined Contributions can trigger actions that offer tangible benefits to vulnerable actors critical to sustaining key components of a country’s economy.

In this chapter, we focus on key concepts that health professionals need to understand in order to use climate data and information effectively. We explain the distinction between climate and weather and introduce some basic principles on the physics of the climate. We then go on to describe the character and behaviour of three key variables, namely temperature, precipitation and humidity. Descriptions of a range of other relevant important climate variables follow. Included is a section on storms that describes how temperature, wind and rainfall interact to create devastating extreme events. The final section discusses how data for different variables can be aggregated in time and space, and identifies challenges that emerge in the process.

World Meteorological Organization (WMO) guidance on verification of operational seasonal forecasts.

This chapter describes how climate varies by location, by considering the effects of altitude, latitude and other aspects of geography on the climate. Weather and climate can vary considerably at almost all timescales, but the amount they do vary differs considerably from place-to-place, and at different times of the year. Sea-surface temperature anomalies affect evaporation and heating or cooling of the overlying air, and the effect can last for weeks, months or even longer, because it takes so much energy to change the temperature of water. Spatial and temporal variations of temperature are much simpler than those of rainfall at virtually all scales. It is important to understand these scales of variability in space and time to obtain some idea of the necessary resolution of data for any analyses of climate–health relationships. The North Atlantic Oscillation is a large-scale pattern of natural climate variability characterized by a seesaw difference in air pressure between the Azores and Iceland.

Potential and real predictive skill of the frequency of extreme rainfall in southeastern South America for the December–February season are evaluated in this paper, finding evidence indicating that mechanisms of climate variability at one time scale contribute to the predictability at another scale; that is, taking into account the interference of different potential sources of predictability at different time scales increases the predictive skill. Part I of this study suggested that a set of daily atmospheric circulation regimes, or weather types, was sensitive to these cross–time scale interferences, conducive to the occurrence of extreme rainfall events in the region, and could be used as a potential predictor. At seasonal scale, a combination of those weather types indeed tends to outperform all the other candidate predictors explored (i.e., sea surface temperature patterns, phases of the Madden–Julian oscillation, and combinations of both). Spatially averaged Kendall’s t improvements of 43% for the potential predictability and 23% for real-time predictions are attained with respect to standard models considering sea surface temperature fields alone. A new subseasonal-to-seasonal predictive methodology for extreme rainfall events is proposed based on probability forecasts of seasonal sequences of these weather types. The cross-validated real-time skill of the new probabilistic approach, as measured by the hit score and the Heidke skill score, is on the order of twice that associated with climatological values. The approach is designed to offer useful subseasonal-to-seasonal climate information to decision-makers interested not only in how many extreme events will happen in the season but also in how, when, and where those events will probably occur.

This report provides peacebuilding practitioners with guidance for accessing and using climate data and information in fragile contexts. It describes some of the challenges to generating, accessing and understanding climate information in contexts of state fragility. It introduces the types of climate information relevant to national and international peacebuilding practitioners operating in fragile contexts and provides a review of relevant and accessible climate data. The report addresses how climate information can be effectively used in a peacebuilding context, and highlights some of the immediate priorities and sequencing needs for fragile states attempting to rebuild their national capacities to generate climate information.

In 2008, the International Federation of Red Cross and Red Crescent Societies (IFRC) used a seasonal forecast for West Africa for the first time to implement an Early Warning, Early Action strategy for enhanced flood preparedness and response. Interviews with disaster managers suggest that this approach improved their capacity and response. Relief supplies reached flood victims within days, as opposed to weeks in previous years, thereby preventing further loss of life, illness, and setbacks to livelihoods, as well as augmenting the efficiency of resource use. This case demonstrates the potential benefits to be realised from the use of medium-to-long-range forecasts in disaster management, especially in the context of potential increases in extreme weather and climate-related events due to climate variability and change. However, harnessing the full potential of these forecasts will require continued effort and collaboration among disaster managers, climate service providers, and major humanitarian donors.

Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.

Procedures for verification of seasonal and longer-range forecasts.

This paper evaluates the quality of real-time seasonal probabilistic forecasts of the extreme 15% tails of the climatological distribution of temperature and precipitation issued by the International Research Institute for Climate and Society (IRI) from 1998 through 2009. IRI’s forecasts have been based largely on a two-tiered multimodel dynamical prediction system. Forecasts of the 15% extremes have been consistent with the corresponding probabilistic forecasts for the standard tercile-based categories; however, nonclimatological forecasts for the extremes have been issued sparingly. Results indicate positive skill in terms of resolution and discrimination for the extremes forecasts, particularly in the tropics. Additionally, with the exception of some overconfidence for extreme above-normal precipitation and a strong cool bias for temperature, reliability analyses suggest generally good calibration. Skills for temperature are generally higher than those for precipitation, due both to correct forecasts of increased probabilities of extremely high (above the upper 15th percentile) temperatures associated with warming trends, and to better discrimination of interannual variability. However, above-normal temperature extremes were substantially underforecast, as noted also for the IRI’s tercile forecasts.

Climate-related disasters are by far the most frequent natural disasters, exacting a heavy toll on people and economies. Their frequency and economic losses have steadily increased over the past few decades, stretching the response capacities of governments and humanitarian organizations. One of the many ways this challenge can be addressed is by making more effective use of the increasing wealth of climate information and tailoring it to the needs of those who could use it, to better predict and prepare for such disasters before they occur. Written in partnership with a range of humanitarian organizations, A Better Climate for Disaster Risk Management is the third in the Climate and Society Publication series. This issue highlights recent advances in the use of climate information to manage risks and improve livelihoods, such as new partnerships and user-designed information platforms. It draws together and analyzes experiences from 17 case studies that capture the current state of knowledge. It also highlights research innovations in technical boxes throughout the publication. A problem-solving framework is used to demonstrate the challenges and opportunities facing disaster risk managers in using climate science with a three step approach: identifying the problem, developing tools, and taking action, reflected in the chapter titles. The case studies and experiences presented in this book draw on a wealth of practical experience from within the humanitarian community. They acknowledge the enormous effort and investment by very many national and local governments, international organizations, and an increasing range of other actors in the field of climate information for disaster risk management. This publication adds to the growing body of knowledge, focusing on the experiences of a number of mostly non-governmental actors, especially the International Federation of Red Cross and Red Crescent Societies, and how through partnerships, they have helped to integrate state of the art climate science and information into improved decision-making. Exploring the use of climate information for disaster risk management, it identifies both the achievements and the obstacles associated with this endeavour. From them are distilled the lessons learned, and a series of recommendations. Of these, effective partnership is highlighted as the single most critical ingredient for success. Climate information that can be acted upon is best created in dialogue between the users and providers, and partnerships between climate scientists and disaster risk managers should promote knowledge sharing, trust, and the development of innovative solutions. Efforts to better apply climate information in disaster risk management should first focus on immediate opportunities and potential ‘quick wins’. Practical engagements can be fostered by initially concentrating on countries and regions with relatively good seasonal forecast skills, and where humanitarian decisions can be influenced to provide large and immediate returns on investment. Disaster risk managers must, however, improve their understanding of the potential as well as the limitations of climate information, as the development of realistic expectations is vital to maintaining trust in the information and those who provide it. Cases demonstrate that when climate information can be integrated into existing decision-making support tools or systems, it becomes an important piece of the information that is considered and taken up in the routine activities of disaster risk managers. The relative contribution that seasonal, decadal, and long-term trends make to current and future climate also needs to be better understood. To achieve the goal of providing relevant climate services to support disaster risk management, climate information providers such as national meteorological services must tailor their information to the problem at hand, either by refining products through iterative interaction with partners or by simplifying the presentation. Although there have been many achievements and advances, much potential remains to be realized. Herein lies the opportunity: to build trust and improve the sharing of knowledge between the providers of climate services, and those who can use those services to enhance disaster risk management, jointly reducing human suffering and achieving more sustainable development.

Reply to a comment on the article: Conditional Exceedance Probabilities. Monthly Weather Review 135 (2010), 363–372 (available in Academic Commons at http://dx.doi.org/10.7916/D8PK0G2S).

The East African humanitarian community is looking for ways to better respond to the challenges presented by climate risks, including climate change, but is struggling to access appropriate and targeted scientific data that can inform their operations. Recent advances in science and technology have produced a variety of new tools for humanitarian organizations working on climate risk management. Humanitarian actors have an enormous opportunity to utilize these tools to inform risk reduction, preparedness and contingency planning, as well as program implementation. Despite such advances, many challenges remain to the practical application of these tools in the humanitarian context. Often times, climate information is too technical or lacks the context necessary for use in humanitarian planning and operations. Thus, climate information must be tailored to specific needs and presented in formats that are readily accessible to these users. In response, the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA), in close collaboration with the International Federation of Red Cross/Red Crescent Societies (IFRC), initiated the development of a 2-day Humanitarian Climate Risk Management Workshop on 23- 24 February 2010. Initiatives that enable interaction can help to bridge the divide between humanitarian practitioners and climate experts; feedback provided by end-users can guide research and development of new prediction technologies and tools, as well as more appropriate packaging of current information. At the same time, humanitarian actors need to evaluate how such information can usefully inform their decision-making at various timescales. The challenge of decision-making under uncertainty must be addressed if climate information is to be used effectively within humanitarian planning, preparedness, and response. This workshop aimed to address such barriers to the use of climate information.

Now that the Regional Climate Outlook Forums in Africa have been operating for over ten years, an evaluation of the skill of these forecasts is possible. For most other regions in which RCOFs have been held there are fewer forecasts available for any detailed diagnostic verification, but many of the lessons learnt from a verification of the Africa RCOF forecasts are relevant globally. In addition, the identification of appropriate verification procedures has relevance globally, since forecasts are presented in similar formats at all the RCOFs. Forecasts are verified from three RCOFs in Africa: for Southern Africa forecasts are verified for the October – December (early-season) and January – March (late-season) summer rainfall season; for the Greater Horn, the target seasons are March – May and September – December; and for West Africa forecasts are for July – September. All three regions indicate some evidence of positive skill, meaning that they contain useful information that could potentially have been used to achieve some form of benefit. In addition to the numerous other benefits, such as the development of capacity within the climate services of the National Meteorological Services in most of Africa, the positive skill provides a powerful endorsement to the RCOF process. However, the forecasts do show clear evidence of systematic errors, and in some cases the positive skill may not be immediately apparent to users. There is thus considerable scope for improvement. The most ubiquitous error is for the forecasters to hedge the forecasts towards high probabilities on the normal category. The probabilities for the normal category are therefore consistently higher than they should be, and the normal rainfall occurred notably much less frequently and extensively than implied by the forecasts. This hedging is an effect of an ongoing deterministic interpretation of the forecasts and the wish to avoid the risk of the forecasts being interpreted as in error by two categories (which is possible only if below- or above-normal rainfall is forecast). In addition to this over-forecasting of the normal category, there is little or no evidence of any skill in forecasting increased probabilities for this category. More generally, the probabilities for all categories typically show poor reliability, and there is a need to implement improved procedures for defining the probabilities. In most cases the poor reliability reflects over-confidence (increases and decreases in probabilities are too large), which points to a need to review the scientific bases for some of the predictions. Over the approximately 10-year verification period, below-normal rainfall was predominant in the Greater Horn in both seasons, in West Africa for the July – September period, and in Southern Africa for January – March. The RCOFs did not provide any clear indications of these trends, which has to be acknowledged as a notable failure. Again, the need for a serious review of the scientific bases for how the forecasts are currently made needs to be undertaken, and an assessment of the potential benefit of making greater use of Global Producing Centre products should be conducted. Apart from these considerations of the skill of the forecasts, ambiguities in the precise meaning of the forecasts occur because of the way in which the forecasts are constructed. Specifically it is not clear whether the forecasts are meant to be interpreted only as regional averages, and, if so, what precisely the regions are over which the averages should be calculated. It is recommended that this ambiguity be addressed by careful consideration of how the forecasts are constructed; specifically, greater consistency is required in the ways in which the forecasts are made for each country before the consensus building step.

Statistical models provide an alternative approach to using dynamical models in seasonal climate forecasting. In statistical models relationships between one set of data, the predictors, and a second set, the predictands, are sought. Common predictands include seasonal mean temperatures and accumulated precipitation, and are typically predicted using antecedent sea surface temperatures primarily within the tropical oceans. Predictions are made on the assumption that historically observed relationships are expected to apply in the future. There are many conditions for such an assumption to be valid, including the need for high-quality datasets to ensure that the historical relationships are robustly measured, and the need for relationships to have a sound theoretical basis. Because of the possibility of identifying spurious relationships between the predictors and the predictands, the statistical model should be tested carefully on independent data. Most statistical models are based on linear regression, which provides a “best guess” forecast under the assumption that a given change in the value of a predictor results in a constant change in the expected value of the predictand regardless of the value of the predictor. Modifications to the linear model can be made or alternative statistical procedures used when there is good reason to expect a relationship to be nonlinear. However, other weaknesses of linear regression may also require these alternatives to be considered seriously. The primary problems with linear regression are multiplicity, multicolinearity, and non-normality of the predictands. Multiplicity refers to the effects of having a large number of candidate predictors: the danger of finding a spurious relationship increases. Multicolinearity arises when more than one predictor is used in the model and there are strong relationships between the predictors which can result in large errors in calculating the parameters of the model. Finally, a linear regression model may not be adequately constructed if the data being predicted have a strongly skewed or otherwise non-Gaussian distribution; seasonally accumulated precipitation often exhibits such problems. Alternative forms of linear and non-linear statistical models can be applied to address such distributional problems.

Humanity recognised millennia ago the importance of climate variability to the sustenance of life, whether that variability was expressed in the form of droughts, floods, heat, cold, or wind. Coping strategies, developed to handle the consequences of climate variability, helped ensure mankind’s survival, although the historic record indicates that not all societies successfully overcame past challenges imposed by long-term droughts, extensive flooding, and the like. Early coping strategies included migration, invasion, appropriation and storage. In addition many, probably most, perhaps all, societies developed indigenous knowledge or belief systems that they felt enabled them to foresee or control those elements of the climate that are so critical for maintaining water and food supplies.

An overview of Regional Climate Outlook Forums.

Although it is impossible to forecast the weather more than a few days in advance, the science of seasonal climate forecasting is premised upon an ability to predict the general weather conditions over a prolonged period of time, without trying to predict the precise weather at any specific time during that period. The forecasting is possible only because sometimes, and primarily within tropical latitudes, the atmosphere is sensitive to unusual conditions at the earth’s surface, and especially at the sea surface. El Niño, and its counterpart La Niña, are the primary examples of such forcing conditions: during El Niño events, much of the equatorial Pacific Ocean is unusually hot (cold during La Niña), and the consequent changes to the heat and moisture supplied to the atmosphere can disrupt weather conditions in many parts of the globe. However, all seasonal climate forecasts involve a great deal of uncertainty, and a key aspect of forecasting at such time scales is to estimate the uncertainty in the prediction reliably. There are two sources of uncertainty in seasonal climate forecasting: the atmosphere is nowhere completely forced by conditions at the surface, but is free to vary according to its own internal dynamics; and the models used to predict the climate system are imperfect. These two sources of uncertainty are addressed by producing a set of model predictions: different initial weather conditions are used to represent the uncertainty from the internal dynamics, and different models to account for the uncertainties arising from imperfect model physics.

When seasonal climate forecasts are expressed probabilistically, it is not possible to answer simple questions such as “how often are the forecasts correct?” The simpler attributes of forecast quality, such as “accuracy” or “correctness” are not applicable to probabilistic forecasts, and instead the main attributes of interest are: reliability, which defines whether the confidence communicated in the forecasts is appropriate; resolution, which defines whether there is any usable information in the forecasts; discrimination, which defines whether the forecasts are discernibly different given different outcomes (somewhat similar to the attribute of resolution); and sharpness, which defines the level of confidence that is communicated in the forecasts (regardless of whether that level is appropriate). How these attributes are measured depends on how the forecasts are expressed. In this chapter these attributes are explained in detail, and representation by various graphical procedures and scoring metrics is described. Partly because there is more than one desirable attribute to good probabilistic forecasts, it is argued that there is no single scoring metric that can adequately summarise forecast quality, and that in many cases graphical procedures also hide important aspects of forecast quality. The aim in this chapter is to provide some guidelines for interpreting and recognising the strengths and limitations of the most important verification tools as applied to seasonal climate forecasts.

Producing a seasonal climate forecast from a dynamical model involves a great deal more than simply running the model and viewing the results. The first problem is to decide which dynamical model(s) should be run given the practical constraints of computing resources. In this chapter the pros and cons of using the more computationally intensive fully coupled models compared to atmosphereonly models are discussed. After running a dynamical model, regardless of its complexity, corrections need to be made for systematic errors because the model’s climatology and that of the observed climate are invariably different. Some simple procedures for correcting these systematic errors are assessed, but more sophisticated methods are advisable to adjust for spatial displacements of the model climate. Since the model predictions represent large spatial averages, and generally are presented as seasonal averages, downscaling may be required to make the forecast relevant for specific locations, and to provide more detailed information about the statistics of weather within the season. Commonly used spatial and temporal downscaling procedures are described. Some procedures for describing the uncertainty in the forecast are discussed (further details are provided in Chapter 9). Evidence is presented that forecasts can be improved by combining outputs from different models. Finally, the reliability of the forecast needs to be determined by verification of a historical set of forecasts.

The enthusiasm for engaging the challenges of Seasonal to Interannual Prediction, both within the disciplines of physical and social sciences and at their interface, was well demonstrated through the energetic engagement of all during the May to June 2005 NATO ASI course, upon which this book is based. Several panel sessions were held during the course, which permitted everyone to offer views within an informal setting; some, not reflected in the main body of the book, are incorporated in this chapter. Little stays stationary in such a fast-developing field, and so, to provide the most advanced position at publication, this summarising chapter has included some of the latest development to supplement the material drawn from the course presentations and the panel discussions. Additionally, a view to the future is offered so as to provide further stimulation to those interested in the fascinating field of Seasonal to Interannual Climate

Because of the inherently chaotic nature of the atmosphere, ensemble simulations are required to characterize a model’s response to the prescribed boundary forcing in probabilistic terms, particularly if the focus is on the probabilities of extreme events. At the same time, substantial computer resources are needed to produce routinely ensemble seasonal climate forecasts of sufficient size to make suitably reproducible estimates of such probabilities. We describe a method for artificially expanding the effective number of members in ensemble climate simulations on a seasonal basis, thereby reducing uncertainty in estimated probability distributions. As described here, the method involves calculating seasonal statistics using monthly values from all possible combinations of ensemble members. Under certain assumptions, this method is able to increase the effective ensemble size of an N-member M-month seasonal forecast by a factor of (asymptotically) M. One key assumption in this regard is that, aside from the effects of prescribed boundary conditions, the month-to-month values from a particular ensemble member are linear independent. This paper describes the behaviour of the ensemble expansion technique using both idealized and actual ensemble forecast data under a variety of conditions, drawing comparisons with an alternative parametric approach for ensemble expansion. A method for testing the assumption of linear independence in model simulations is also presented.

The potential for long‐range prediction of Zimbabwe summer rainfall is investigated using an analysis of variance approach. It is assumed that the variance between seasons (inter‐annual variability) is made up of two components: the climate noise (intra‐seasonal variability) and signal (any variance above the noise). The magnitude of the climate noise is estimated by computing the variance of time‐averaged rainfall data based on a statistical model whose parameters are derived from observed daily values at each station. Results from the study indicate that up to approximately 70% of the total variance in Zimbabwe summer rainfall is potentially predictable at long range. Predictability is greatest during the last half of the rainy season, January to March, and much lower during October to December. The south section of the country shows relatively more predictability than the north. Comparisons between signal and noise are discussed in the context of long‐range predictability. The natural variability (noise) is proposed as a lower limit of the standard error of the estimate for any long‐range precipitation forecast for the country.

Extreme phases of the El Niño–Southern Oscillation (ENSO) phenomenon have been blamed for precipitation anomalies in many areas of the world. In some areas the probability of above–normal precipitation may be increased during warm or cold events, while in others below–normal precipitation may be more likely. The percentages of times that seasonal precipitation over land areas was above, near, and below normal during the eight strongest El Niño and La Niña episodes are tabulated, and the significance levels of the posterior probabilities are calculated using the hypergeometric distribution. These frequencies may provide a useful starting point for probabilistic climate forecasts during strong ENSO events. Areas with significantly high or low frequencies or above– or below–normal precipitation are highlighted, and attempts are made to estimate the proportion of land areas with significant ENSO–related precipitation signals. There is a danger of overstating the global impact of ENSO events because only about 20%–30% of land areas experience significantly increased probabilities of above– or below–normal seasonal precipitation during at least some part of the year. Since different areas are affected at different times of the year, the fraction of global land affected in any particular season is only about 15%–25%. The danger of focusing on the impact of only warm–phase events is emphasized also: the global impact of La Niña seems to be at least as widespread as that of El Niño. Furthermore, there are a number of notable asymmetries in precipitation responses to El Niño and La Niña events. For many areas it should not be assumed that the typical climate anomaly of one ENSO extreme is likely to be the opposite of the other extreme. A high frequency of above–normal precipitation during strong El Niño conditions, for example, does not guarantee a high frequency of below–normal precipitation during La Niña events, or vice versa. On a global basis El Niño events are predominantly associated with below–normal seasonal precipitation over land, whereas La Niña events result in a wider extent of above–normal precipitation.

Spatial patterns of inter-annual summer rainfall variability over Zimbabwe are investigated using principal component analysis for the period 1920-96. Inter-annual rainfall anomalies for all years and stratified according to warm El Niño - Southern Oscillation (ENSO) events are used in the study. The varimax rotated PC loadings subdivide the country into three regions, north, southwest and southeast. Because of the high correlation between two of the regions, only two regions, north and south, consistent with the underlying physical processes are retained. The two leading PCs are consistent with the main rain-producing synoptic weather systems affecting the country during austral summer. Inter-comparison of the rainfall anomaly time series from the two regions reveals that the major controlling factors influencing inter-annual rainfall variability are possibly of a global or hemispheric nature. The spatially coherent regions have practical use in the development and application of long-range seasonal climate forecasts and climate change studies. The file associated with this record contains pages 430-31 of this article.

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.

Most estimates of the skill of atmospheric general circulation models (AGCMs) for forecasting seasonal climate anomalies have been based on simulations with actual observed sea surface temperatures (SSTs) as lower boundary forcing. Similarly estimates of the climatological response characteristics of AGCMs used for seasonal-to-interannual climate prediction frequently rest on historical simulations using "perfect" SST forecasts. This paper examines the errors and biases introduced into the seasonal climate response of an AGCM forced with persisted SST anomalies, which are generally considered to constitute a good prediction of SST in the first 3-month season. However, the added uncertainty introduced by the predicted SST anomalies weakens, and in some cases nullifies, the skill of atmospheric predictions that is possible given perfect SST forcing. The use of persisted SST anomalies also leads to changes in local signal-to-noise characteristics. Thus, it is argued that seasonal-to-interannual forecasts using AGCMs should be interpreted relative to historical runs that were subject to the same strategy of boundary forcing used in the current forecast in order to properly account for errors and biases introduced by the particular SST prediction strategy. Two case studies are examined to illustrate how the sensitivity of the climate response to predicted SSTs may be used as a diagnostic to suggest improvements to the predicted SSTs.

The El Niño phenomenon involves a large‐scale warming of the equatorial eastern and central Pacific Ocean. Recent developments in the El Niño–Southern Oscillation (ENSO) phenomenon have raised concerns about climate change. In this review paper, these recent developments are critically assessed and forecasts of possible future changes are reviewed. Since the late‐1970s, El Niño episodes have been unusually recurrent, while the frequency of strong La Niña events has been low. Prolonged/recurrent warm event conditions of the first half of the 1990s were the result of the persistence of an anomalously warm pool near the date line, which, in turn, may be part of an abrupt warming trend in tropical sea‐surface temperatures that occurred in the late‐1970s. The abrupt warming of tropical sea‐surface temperatures has been attributed to the enhanced‐greenhouse effect, but may be indicative of inter‐decadal variability: earlier changes in the frequency of ENSO events and earlier persistent El Niño and La Niña sequences have occurred. Most forecasts of ENSO variability in a doubled‐CO2 climate suggest that the recent changes in the tropical Pacific are anomalous. Of potential concern, however, is a possible reduction in the predictability of ENSO events given a warmer background climate. El Niño events usually are associated with below‐normal rainfall over much of southern Africa. Mechanisms for this influence on southern African climate are discussed, and the implications of possible changes in ENSO variability on the climate of the region are assessed. Recent observed changes in southern African climate and their possible relationships with trends in ENSO variability are investigated. The El Niño influence on rainfall over southern Africa occurs largely because of a weakening of tropical convection over the subcontinent. A warming of the Indian Ocean during El Niño events appears to be important in providing a teleconnection from the equatorial Pacific Ocean. The abrupt warming of the tropical Pacific and Indian oceans in the late‐1970s is probably partly responsible for increasing air temperatures over southern Africa, and may have contributed to a prolongation of predominantly dry conditions. A return to a wet phase appears to have occurred, despite the persistence of anomalously high sea‐surface temperatures associated with the late‐1970s warming, and a record‐breaking El Niño in 1997/98.

Sea‐surface temperature (SST) variations of the oceans surrounding southern Africa are associated with seasonal rainfall variability, especially during austral summer when the tropical atmospheric circulation is dominant over the region. Because of instabilities in the linear association between summer rainfall over southern Africa and SSTs of the tropical Indian Ocean, the skilful prediction of seasonal rainfall may best be achieved using physically based models. A two‐tiered retro‐active forecast procedure for the December–February (DJF) season is employed over a 10‐year period starting from 1987/1988. Rainfall forecasts are produced for a number of homogeneous regions over part of southern Africa. Categorized (below‐normal, near‐normal and above‐normal) statistical DJF rainfall predictions are made for the region to form the baseline skill level that has to be outscored by more elaborate methods involving general circulation models (GCMs). The GCM used here is the Centre for Ocean–Land–Atmosphere Studies (COLA) T30, with predicted global SST fields as boundary forcing and initial conditions derived from the National Centres for Environmental Prediction (NCEP) reanalysis data. Bias‐corrected GCM simulations of circulation and moisture at certain standard pressure levels are downscaled to produce rainfall forecasts at the regional level using the perfect prognosis approach. In the two‐tiered forecasting system, SST predictions for the global oceans are made first. SST anomalies of the equatorial Pacific (NIÑO3.4) and Indian oceans are predicted skilfully at 1‐ and 3‐month lead‐times using a statistical model. These retro‐active SST forecasts are accurate for pre‐1990 conditions, but predictability seems to have weakened during the 1990s. Skilful multi‐tiered rainfall forecasts are obtained when the amplitudes of large events in the global oceans (such as El Niño and La Niña episodes) are described adequately by the predicted SST fields. GCM simulations using persisted August SST anomalies instead of forecast SSTs produce skill levels similar to those of the baseline for longer lead‐times. Given high‐skill SST forecasts, the scheme has the potential to provide climate forecasts that outscore the baseline skill level substantially.

The skill of global-scale sea surface temperature forecasts using a statistically based linear forecasting technique is investigated. Canonical variates are used to make monthly sea surface temperature anomaly forecasts using evolutionary and steady-state features of antecedent sea surface temperatures as predictors. Levels of forecast skill are investigated over several months' lead time by comparing the model performance with a simple forecast strategy involving the persistence of sea surface temperature anomalies. Forecast skill is investigated over an independent test period of 18 yr (1982/83–1999/2000), for which the model training period was updated after every 3 yr. Forecasts for the equatorial Pacific Ocean are a significant improvement over a strategy of random guessing, and outscore forecasts of persisted anomalies beyond lead times of about one season during the development stages of the El Niño–Southern Oscillation phenomenon, but only outscore forecasts of persisted anomalies beyond 6 months' lead time during its most intense phase. Model predictions of the tropical Indian Ocean outscore persistence during the second half of the boreal winter, that is, from about December or January, with maximum skill during the March–May spring season, but poor skill during the autumn months from September to November. Some loss in predictability of the equatorial Pacific and Indian Oceans is evident during the early and mid-1990s, but forecasts appear to have improved in the last few years. The tropical Atlantic Ocean forecast skill has generally been poor. There is little evidence of forecast skill over the midlatitudes in any of the oceans. However, during the spring months significant skill has been found over the Indian Ocean as far south as 20°S and over the southern North Atlantic as far north as 30°N, both of which outscore persistence beyond a lead time of less than about one season.

A multi-tiered forecast procedure is employed to simulate real-time operational seasonal forecasts of categorized (below-normal, near-normal and above-normal) streamflow at the inlets of twelve dams of the Vaal and upper Tugela river catchments in South Africa. Forecasts are made for the December to February (DJF) season over an 8-year independent period from 1987/1988 to 1994/1995. A physically based model of the atmosphere system, known as a general circulation model (GCM), is used to simulate atmospheric variability over southern Africa, the output of which is statistically downscaled to streamflow. The GCM used is the COLA T30, and is forced at the boundary with predicted monthly-mean global sea-surface temperatures. The monthly-mean sea-surface temperature fields are first predicted over lead-times of several months using a canonical correlation analysis (CCA) model. GCM simulations are then obtained for an area including most of southern Africa and adjacent oceans. The GCM simulations are downscaled to catchment level from coarse resolution gridded climate variables, using a perfect prognosis approach: bias-corrected GCM simulations are substituted into the perfect prognosis equations to provide the downscaled categorized streamflow forecasts. Although surface characteristics of each catchment that affect the variability of streamflow are not considered in the proposed downscaling system, successful forecasts of streamflow categories were obtained for some of the years forecast independently. The scheme's operational utility is thus demonstrated, albeit over short lead-times.

Southern Africa lies within the subtropical high pressure belt of the Southern Hemisphere between about 15° and 35° S (Figure 8.1). Subsidence of air in large anticyclones predominates for most of the year, resulting in arid or semiarid conditions throughout much of the subcontinent. During the first half of summer (October-December), the southward migration of the locus of tropical convection provides early-season rainfall to the northern parts of the subcontinent, while the development of a subtropical trough within the quasi-permanent high pressure belt facilitates the occurrence of rainfall farther south from midlatitude systems. By January, the tropical atmosphere is usually dominant over most of the subcontinent, often bringing good rains, especially when links with westerly troughs form. The interannual rainfall variability of the region is high, with the coefficient of variation exceeding 40 per cent in the drier western areas. Droughts are an inherent feature of the climate, and water resources are under growing pressure from population and industrial expansion, being particularly strained during drought years when demand for water increases. Given the high degree of interannual rainfall variability in the southern African region, skilful seasonal forecasts could greatly assist in water resource planning and the amelioration of drought and flood impacts. Since the 1991-2 drought, long-range seasonal forecasts for southern Africa using statistical methods have been produced by universities, the national meteorological services, and drought monitoring centres in South Africa and neighbouring countries. More recently, statistical forecasts have been supplemented by real-time general circulation model ensemble seasonal forecasts. In this chapter, progress in the understanding of droughts and predictability over southern Africa is reviewed.

Extreme rainfall events can have severe impacts on society, so possible long-term changes in the intensity of extreme events are of concern. Testing for long-term changes in the intensity of extreme events is complicated by data inhomogeneities resulting from site and instrumentation changes. Using rainfall data from stations in South Africa that have not involved site relocations, but which have not been tested for inhomogeneities resulting from changes in instrumentation, a method of testing for changes in the intensity of extreme events is adopted. Significant increases in the intensity of extreme rainfall events between 1931–1960 and 1961–1990 are identified over about 70% of the country. The intensity of the 10-year high rainfall events has increased by over 10% over large areas of the country, except in parts of the north-east, north-west and in the winter rainfall region of the south-west. Percentage increases in the intensity of high rainfall events are largest for the most extreme events. While some inhomogeneities remain in the data used, the observed changes in the intensity of extreme rainfall events over South Africa are thought to be at least partly real.

Southern hemisphere and South African regional air temperature anomalies for the period 1860–1996 are compared to borehole‐derived surface rock‐temperature anomalies to assess the extent to which surface rock temperatures reflect possible global warming in South Africa. The warming of the southern hemisphere since the mid‐nineteenth century is evident in the regional air temperatures for South Africa. Following a temperature increase to a maximum in the 1920s, the climate cooled until strong warming recommenced in the mid 1970s. Highest temperatures have been experienced in the 1990s. Borehole‐derived surface rock temperatures followed a similar pattern, but with a lag of a few decades in the case of the 1920s maximum. The overall warming trend is clearly evident in the rock temperatures. Despite uncertainties in the different data sets, an encouraging degree of agreement exists between the increase in rock temperatures during the twentieth century, the corresponding regional South African air temperature increase and the southern hemisphere counterpart.

The southern African region is susceptible to climatic extremes and particularly to extended dry periods. Possible changes in the probability of dry years under doubled‐CO2 conditions are examined using output from the CSIRO nine‐level general circulation model. Changes in annual mean rainfall are not expected to be significant. However, the model simulates an increase in the probability of dry years in the tropics, to the south‐west of the subcontinent, as well as over the western and eastern parts of South Africa and southern Mozambique, where large percentage increases in the most intense dry spells are indicated. A decrease in the frequency of dry years is simulated over much of the interior of the subcontinent south of 10°S. In regions where the frequency of dry years decreases, the most severe events occur less often. The CSIRO nine‐level model indicates a shift in the frequency distribution of daily rainfall events under doubled‐CO2 conditions. A small change in the frequency distribution of daily rainfall events may have further implications for the frequency of mid‐summer droughts during the peak summer rainfall period of December–February. Increases in the frequency of mid‐summer droughts are simulated over the eastern part of the subcontinent south of 20°S.

Quasi-periodicities in annual rainfall totals over southern Africa have been identified; in particular, an approximately 18-year cycle may be related to interdecadal variability in sea-surface temperatures in the eastern equatorial Pacific and central Indian Oceans. A 10-year cycle along the south coast is related to variability in standing wave 3. Atmospheric anomalies associated with wet and dry years can be related to changes in the frequency, intensity and persistence of important rainfall-producing weather systems and highlight the significance of the strength of the continental heat low and the preferred locations and amplitudes of the westerly troughs. El Niño Southern Oscillation events and sea-surface temperature anomalies in the Indian and South Atlantic Oceans can influence both the tropical and the temperate atmospheric circulation and moisture fluxes over the subcontinent and thus are significant influences on rainfall variability. Evidence for long-term climatic change is not as definitive as in the Sahel, although there are indications of desiccation in some areas since the late-1970s. Increases in temperatures are of approximately the same magnitude as the hemispheric trends and may be attributable to the enhanced greenhouse effect.

A general circulation model simulation is used to investigate possible changes in rainfall over southern Africa resulting from a doubling of atmospheric carbon dioxide. Simulated increases in rainfall intensity are found to be a spatially coherent and an apparently less regionally dependent signal of climatic change than changes in annual means or number of rain‐days. Accordingly, increases in both the frequency and intensity of extreme daily rainfall events are simulated throughout most of the subcontinent. Simulated increases in the intensity of the lowest frequency floods are shown to be particularly severe, suggesting that greenhouse‐related climatic change may be most detectable through an increase in extreme flood events rather than changes in long‐term means. Similar results are evident when changes in the frequency and intensity of prolonged rainfall events, measured over a period of five consecutive days, are analysed. All results are qualitatively similar to those for the Australian region, except that the model's sensitivity to sharp changes in topography over southern Africa is highlighted.

The main features of sea‐surface temperature variability in the South Atlantic and the south‐west Indian Oceans are identified and their interaction with the Southern Oscillation discussed. Most of the variance is explained by coherent features of variability in the South Atlantic Ocean. Tropical and subtropical features dominate the variance, but this may be partly a reflection of data availability. Many of the principal components are associated with rainfall over southern Africa and the strongest associations occur with sea‐surface temperatures in the western equatorial Indian Ocean, the Agulhas system, and the central South Atlantic Ocean.

The importance of defining confidence intervals for sample statistics that are used to estimate characteristics of the parent population(s) is emphasised. Not all sample statistics are unbiased estimators or have normally distributed sampling distributions and so it is not always easy to reflect the reliability of the estimator. In such cases, Efron's “bias corrected percentile method”, which uses bootstrap samples to estimate the bias and makes no assumptions about the distribution of the sample statistic can be used to define confidence limits for the population parameter. The method is explained and the procedure for calculating the confidence limits is outlined. As an example, bootstrap confidence limits calculated for the maximum correlation between the Southern Oscillation Index and rainfall at South African stations over the period 1935–1983 suggest that the sample correlation is an unreliable measure of the true association. One possible reason for this is that the association is thought to have broken down during the 1940s. However, the reliability of the estimator does not seem to improve when confidence limits are calculated for the 30-year period 1954–1983. It is possible that the width of the confidence interval is an indication of more than one distinct statistical population.