Climate variability and trends: drivers

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

Climate forecasts for early warning: up to six months in advance

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

Climatic change over the Lowveld of South Africa

There has been a 38% decrease in expected annual rainfall totals over the Lowveld, in the eastern part of South Africa, during the last two decades. The downward trend in mean annual rainfall is not replicated in the rest of the summer rainfall region above the escarpment. Rainfall variability over the Lowveld has been increasing since about the 1950s, although the increase in variability appears to have been slowing down in more recent years. Changes in the frequency and intensity of El Niño/Southern Oscillation extreme events are only partly responsible for the observed desiccation and increase in rainfall variability. The CSIRO 9-level general circulation model simulates, for 2 × CO2 conditions, an insignificant decrease of 10% in the annual mean and a slight increase in the inter-annual variability of rainfall over the Lowveld. Other general circulation models likewise simulate only small changes in annual mean rainfall over the region. However, the simulated increase in rainfall variability by the CSIRO 9-level model is likely to be conservative since the model, being linked to a slab ocean, is unable to represent important features of ocean-atmosphere coupling in the region. Significant changes in the frequencies of extreme drought events and of heavy rains in the Lowveld are likely to occur even with only small changes in the rainfall climatology of the region

Sea-surface temperature - South African rainfall associations 1910 - 1989

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

Seasonal forecasting of South African rainfall using a non-linear discriminant analysis model

Statistical models have been used to provide operational seasonal forecasts of rainfall over southern Africa since 1992. The Climatology Research Group has been using a quadratic discriminant analysis model since November 1994. The model relates rainfall over different areas of South Africa to principal components of sea‒surface temperatures in the Indian, South Atlantic and Pacific Oceans. Details of the model are described in this paper. High forecast‒skill levels can be achieved for much of the country throughout the year. The mostly successful performance of the model over a 15‒year independent testing period indicates that the model can be used successfully in an operational environment

Seasonal and longer-range forecasts

Procedures for verification of seasonal and longer-range forecasts

Climate basics

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

On using "climatology" as a reference strategy in the Brier and ranked probability skill scores

The Brier and ranked probability skill scores are widely used as skill metrics of probabilistic forecasts of weather and climate. As skill scores, they compare the extent to which a forecast strategy outperforms a (usually simpler) reference forecast strategy. The most widely used reference strategy is that of ‘‘climatology,’’ in which the climatological probability (or probabilities in the case of the ranked probability skill score) of the forecast variable is issued perpetually. The Brier and ranked probability skill scores are often considered harsh standards. It is shown that the scores are harsh because the expected value of these skill scores is less than 0 if nonclimatological forecast probabilities are issued. As a result, negative skill scores can often hide useful information content in the forecasts. An alternative formulation of the skill scores based on a reference strategy in which the outcome is independent of the forecast is equivalent to using randomly assigned probabilities but is not strictly proper. Nevertheless, positive values of the Brier skill score with random guessing as a strategy correspond to positive-sloping reliability curves, which is intuitively appealing because of the implication that the conditional probability of the forecast event increases as the forecast probability increases

Position Paper: Verification of African RCOF Forecasts

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