Provide an overview of basic concepts in climate and a common understanding of what climate information is and its limitations.
One of the main issues of multidisciplinary research is that each discipline has its own approaches, methods and terminology, shaped during the development period of the discipline. Those are most often dictated by availability of the data and data acquisition methods, which led the discipline in given direction, sometime influenced by personal choices of people having contributed to the discipline. This lecture introduces the basic concepts in climatology to enable the participants from the Public Health Sector to efficiently interact with the Climate and Meteorological Community. For example, the notion of scale (spatial and temporal) is central to understanding climate and climate analyses and leads automatically to the distinction between climate variability and climate change (e.g., ENSO). Understanding climate/meteorological data acquisition methods and sources, and related constraints on available information as well as the basic distinction between data and information are also necessary steps to build a common understanding of what is possible. The most common analysis methods used in climate sciences will be introduced with emphasis on the importance of scale adequacy for the problem at stake.
FAQ 1.1, 1.2,1.3,2.1, 6.1, 6.2, 10.1
Available in English and Spanish, with key definitions
Provide the participants with an understanding of the rationale behind different types of predictions and projections with an emphasis on the interpretation and limitations of the available information
Climate forecasts or projections are often misinterpreted due to their probabilistic format, often omitted in sectoral applications. There is more and more interest in health impact of the future climate so it is important that the current generation of Public Health professionals understands what the projections can or cannot tell us. The lecture is aimed at explaining why forecasts/projections can only be produced in a probabilistic format, which, in fact, attempts to quantify the uncertainty attached to the forecast output. Sources of uncertainty as well as the main forecasting methods will be presented. We will devote some time to a practical interpretation of two examples of forecasts: the seasonal forecast and the Climate Change scenario. An important element for the decision process, forecast verification, will also be briefly introduced.
IRI Tutorials on forecasting:
IPCC 4th Assessment: Introduction to regional projections in chapter 11 http://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch11s11-1-2.html
Associated with National Academy of Sciences, report not recommended
Understand the limitations of climatological data sets before performing data analysis.
Several studies have considered the impact of climate change, and temperature in particular, on the distribution and incidence of malaria in the highland regions of East Africa. The results, however, often led to different conclusions. This was in part related to the fact that they typically used different climate datasets which were either interpolated analyses based on station observations or an insufficient set of station observations, or length of record, for the specific areas of interest.
It is indeed a critical issue to understand the climate (or health) data being used in any study, including limitations in using such data before conducting any analysis. This includes the issue of data quality but also using the appropriate time scale of information (e.g., daily versus monthly rainfall data) for the health question being considered. One needs to take into account the caveats to using gridded data derived from point observations, for example, to avoid drawing potentially inappropriate conclusions from the analysis. Indeed, any analysis should begin with a simple, exploratory step that can subsequently be followed by more sophisticated methods. It is recommended that when undertaking interdisciplinary studies that experts from across disciplines are involved to help minimize misinterpretation of the datasets being used.
This lecture will illustrate these points through considering the analysis of the relation between malaria and temperature in the highlands of Kenya.